From 5795d6c03de387f419d39a2b047d00aad70a73e5 Mon Sep 17 00:00:00 2001 From: Sarah Teichman Date: Thu, 18 Jun 2026 10:26:45 -0700 Subject: [PATCH 01/11] add new chapters after data types before data uses --- 01c-how_to_collect_data.Rmd | 5 +++++ 01d-how_to_acquire_data.Rmd | 5 +++++ _bookdown.yml | 2 ++ 3 files changed, 12 insertions(+) create mode 100644 01c-how_to_collect_data.Rmd create mode 100644 01d-how_to_acquire_data.Rmd diff --git a/01c-how_to_collect_data.Rmd b/01c-how_to_collect_data.Rmd new file mode 100644 index 0000000..e434b48 --- /dev/null +++ b/01c-how_to_collect_data.Rmd @@ -0,0 +1,5 @@ +# How to Collect Clinical Data + +## Learning Objectives + +## Summary diff --git a/01d-how_to_acquire_data.Rmd b/01d-how_to_acquire_data.Rmd new file mode 100644 index 0000000..3e9c7ea --- /dev/null +++ b/01d-how_to_acquire_data.Rmd @@ -0,0 +1,5 @@ +# How to Acquire Clinical Data + +## Learning Objectives + +## Summary diff --git a/_bookdown.yml b/_bookdown.yml index e64f20c..9eb9ae1 100644 --- a/_bookdown.yml +++ b/_bookdown.yml @@ -5,6 +5,8 @@ rmd_files: ["index.Rmd", "00-intro.Rmd", "01-data_types.Rmd", "01b-specific_data_types.Rmd", + "01c-how_to_collect_data.Rmd", + "01d-how_to_acquire_data.Rmd" "02-data_uses.Rmd", "03-data_management.Rmd", "04-appendixI.Rmd", From cd62133bac5b48e8802ffbb008114182b68f02a7 Mon Sep 17 00:00:00 2001 From: Sarah Teichman Date: Thu, 18 Jun 2026 10:28:29 -0700 Subject: [PATCH 02/11] move how to acquire data header from data types chapter, move subheading to new how to acquire data chapter --- 01-data_types.Rmd | 6 ------ 01d-how_to_acquire_data.Rmd | 2 ++ 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/01-data_types.Rmd b/01-data_types.Rmd index edc15f5..afa93af 100644 --- a/01-data_types.Rmd +++ b/01-data_types.Rmd @@ -72,12 +72,6 @@ ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0Ge When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [@Dhir2008] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. - -## How to acquire clinical data - -### Secondary Sources - - ## Description of data ### File types diff --git a/01d-how_to_acquire_data.Rmd b/01d-how_to_acquire_data.Rmd index 3e9c7ea..f5340d8 100644 --- a/01d-how_to_acquire_data.Rmd +++ b/01d-how_to_acquire_data.Rmd @@ -2,4 +2,6 @@ ## Learning Objectives +## Secondary Sources + ## Summary From d8b34ef6bd6009ed9d976c90f1120465d9dbb0b8 Mon Sep 17 00:00:00 2001 From: Sarah Teichman Date: Thu, 18 Jun 2026 10:50:32 -0700 Subject: [PATCH 03/11] adding outline for collecting and acquiring data chapters --- 01c-how_to_collect_data.Rmd | 10 ++++++++++ 01d-how_to_acquire_data.Rmd | 6 ++++++ 2 files changed, 16 insertions(+) diff --git a/01c-how_to_collect_data.Rmd b/01c-how_to_collect_data.Rmd index e434b48..4e3ed38 100644 --- a/01c-how_to_collect_data.Rmd +++ b/01c-how_to_collect_data.Rmd @@ -2,4 +2,14 @@ ## Learning Objectives +## Case Report Forms (CRFs) + +## Clinical Studies and Trials + +## Retrospective Analyses + +Does this belong in collecting data or acquiring data? + +## Research Design Considerations + ## Summary diff --git a/01d-how_to_acquire_data.Rmd b/01d-how_to_acquire_data.Rmd index f5340d8..e5d0cb6 100644 --- a/01d-how_to_acquire_data.Rmd +++ b/01d-how_to_acquire_data.Rmd @@ -2,6 +2,12 @@ ## Learning Objectives +## Electronic Health Records + +## Medical Databases (?) + +## Registries (?) + ## Secondary Sources ## Summary From 610779595c944c73caf42cd95e503463a63900aa Mon Sep 17 00:00:00 2001 From: Sarah Teichman Date: Thu, 18 Jun 2026 13:14:56 -0700 Subject: [PATCH 04/11] moving some pieces around into collecting and acquiring data chapters and rendering course --- .../6F8C099B/bibliography-index/biblio-files | 2 + .../bibliography-index/biblio-refblock | 0 .../6F8C099B/bibliography-index/biblio.json | 1471 +++++++++++++++++ .../bookdown-crossref/00-intro.Rmd.xref | 5 + 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+1781029074:/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/packages.bib diff --git a/.Rproj.user/6F8C099B/bibliography-index/biblio-refblock b/.Rproj.user/6F8C099B/bibliography-index/biblio-refblock new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/6F8C099B/bibliography-index/biblio.json b/.Rproj.user/6F8C099B/bibliography-index/biblio.json new file mode 100644 index 0000000..d3ffb39 --- /dev/null +++ b/.Rproj.user/6F8C099B/bibliography-index/biblio.json @@ -0,0 +1,1471 @@ +{ + "sources": [ + { + "URL": "https://www.nia.nih.gov/health/clinical-trials-and-studies/what-are-clinical-trials-and-studies", + "container-title": "National Institute on Aging", + "id": "What_are_clinical_trials_2023", + "issued": { + "date-parts": [ + [ + 2023, + 3 + ] + ] + }, + "title": "What are clinical trials and studies?", + "type": "" + }, + { + "DOI": "10.1037/amp0001196", + "ISSN": "1935-990X, 0003-066X", + "URL": "https://doi.apa.org/doi/10.1037/amp0001196", + "abstract": "Sensors, including accelerometer-based and electronic adherence monitoring devices, have transformed health data collection. Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medicationtaking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technologyrelated vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care.", + "accessed": { + "date-parts": [ + [ + 2025, + 4, + 22 + ] + ] + }, + "author": [ + { + "family": "Psihogios", + "given": "Alexandra M." + }, + { + "family": "King-Dowling", + "given": "Sara" + }, + { + "family": "Mitchell", + "given": "Jonathan A." + }, + { + "family": "McGrady", + "given": "Meghan E." + }, + { + "family": "Williamson", + "given": "Ariel A." + } + ], + "container-title": "American Psychologist", + "id": "psihogios_ethical_2024", + "issue": "1", + "issued": { + "date-parts": [ + [ + 2024, + 1 + ] + ] + }, + "page": "39-51", + "title": "Ethical considerations in using sensors to remotely assess pediatric health behaviors.", + "type": "article-journal", + "volume": "79" + }, + { + "URL": "https://division-research.brown.edu/research-cycle/proposal/agency-specific/nih/clinical-trials", + "container-title": "Division of Research at Brown University", + "id": "NIHClinicalTrialsProspectively", + "title": "NIH clinical trials", + "type": "" + }, + { + "URL": "https://grants.nih.gov/policy-and-compliance/policy-topics/clinical-trials/definition", + "id": "CTDefinition", + "title": "NIH’s definition of a clinical trial", + "type": "" + }, + { + "ISBN": "978-0-19-966588-4", + "author": [ + { + "family": "Drummond", + "given": "M. F." + }, + { + "family": "Sculpher", + "given": "M. J." + }, + { + "family": "Claxton", + "given": "K." + }, + { + "family": "Stoddart", + "given": "G. L." + }, + { + "family": "Torrance", + "given": "G. 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"2574-3805", + "URL": "https://doi.org/10.1001/jamanetworkopen.2024.32143", + "author": [ + { + "family": "Kaphingst", + "given": "Kimberly A." + }, + { + "family": "Kohlmann", + "given": "Wendy K." + }, + { + "family": "Lorenz Chambers", + "given": "Rachelle" + }, + { + "family": "Bather", + "given": "Jemar R." + }, + { + "family": "Goodman", + "given": "Melody S." + }, + { + "family": "Bradshaw", + "given": "Richard L." + }, + { + "family": "Chavez-Yenter", + "given": "Daniel" + }, + { + "family": "Colonna", + "given": "Sarah V." + }, + { + "family": "Espinel", + "given": "Whitney F." + }, + { + "family": "Everett", + "given": "Jessica N." + }, + { + "family": "Flynn", + "given": "Michael" + }, + { + "family": "Gammon", + "given": "Amanda" + }, + { + "family": "Harris", + "given": "Adrian" + }, + { + "family": "Hess", + "given": "Rachel" + }, + { + "family": "Kaiser-Jackson", + "given": "Lauren" + }, + { + "family": "Lee", + "given": "Sang" + }, + { + "family": "Monahan", + "given": 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support", + "type": "article-journal", + "volume": "14" + }, + { + "URL": "https://github.com/rstudio/rmarkdown", + "author": [ + { + "family": "Allaire", + "given": "JJ" + }, + { + "family": "Xie", + "given": "Yihui" + }, + { + "family": "McPherson", + "given": "Jonathan" + }, + { + "family": "Luraschi", + "given": "Javier" + }, + { + "family": "Ushey", + "given": "Kevin" + }, + { + "family": "Atkins", + "given": "Aron" + }, + { + "family": "Wickham", + "given": "Hadley" + }, + { + "family": "Cheng", + "given": "Joe" + }, + { + "family": "Chang", + "given": "Winston" + }, + { + "family": "Iannone", + "given": "Richard" + } + ], + "id": "rmarkdown2021", + "issued": { + "date-parts": [ + [ + 2021 + ] + ] + }, + "note": "R package version 2.10", + "title": "Rmarkdown: Dynamic documents for r", + "title-short": "Rmarkdown", + "type": "book" + }, + { + "DOI": "10.2146/ajhp170025", + "ISSN": "1079-2082", + "URL": "https://doi.org/10.2146/ajhp170025", + "author": [ + { + "family": 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J." + }, + { + "family": "Grolemund", + "given": "Garrett" + } + ], + "id": "Xie2018", + "issued": { + "date-parts": [ + [ + 2018 + ] + ] + }, + "note": "ISBN 9781138359338", + "publisher": "Chapman; Hall/CRC", + "publisher-place": "Boca Raton, Florida", + "title": "R markdown: The definitive guide", + "title-short": "R markdown", + "type": "book" + }, + { + "URL": "https://bookdown.org/yihui/rmarkdown-cookbook", + "author": [ + { + "family": "Xie", + "given": "Yihui" + }, + { + "family": "Dervieux", + "given": "Christophe" + }, + { + "family": "Riederer", + "given": "Emily" + } + ], + "id": "Xie2020", + "issued": { + "date-parts": [ + [ + 2020 + ] + ] + }, + "note": "ISBN 9780367563837", + "publisher": "Chapman; Hall/CRC", + "publisher-place": "Boca Raton, Florida", + "title": "R markdown cookbook", + "type": "book" + }, + { + "DOI": "10.1037/amp0001196", + "ISSN": "1935-990X, 0003-066X", + "URL": "https://doi.apa.org/doi/10.1037/amp0001196", + "abstract": "Sensors, including accelerometer-based and electronic adherence monitoring devices, have transformed health data collection. Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medicationtaking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technologyrelated vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care.", + "accessed": { + "date-parts": [ + [ + 2025, + 4, + 22 + ] + ] + }, + "author": [ + { + "family": "Psihogios", + "given": "Alexandra M." + }, + { + "family": "King-Dowling", + "given": "Sara" + }, + { + "family": "Mitchell", + "given": "Jonathan A." + }, + { + "family": "McGrady", + "given": "Meghan E." + }, + { + "family": "Williamson", + "given": "Ariel A." + } + ], + "container-title": "American Psychologist", + "id": "psihogios_ethical_2024", + "issue": "1", + "issued": { + "date-parts": [ + [ + 2024, + 1 + ] + ] + }, + "page": "39-51", + "title": "Ethical considerations in using sensors to remotely assess pediatric health behaviors.", + "type": "article-journal", + "volume": "79" + }, + { + "DOI": "10.1111/j.1365-2125.2012.04167.x", + "ISSN": "0306-5251, 1365-2125", + "URL": "https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/j.1365-2125.2012.04167.x", + "abstract": "Interest in patient adherence has increased in recent years, with a growing literature that shows the pervasiveness of poor adherence to appropriately prescribed medications. However, four decades of adherence research has not resulted in uniformity in the terminology used to describe deviations from prescribed therapies. The aim of this review was to propose a new taxonomy, in which adherence to medications is conceptualized, based on behavioural and pharmacological science, and which will support quantifiable parameters. A systematic literature review was performed using MEDLINE, EMBASE, CINAHL, the Cochrane Library and PsycINFO from database inception to 1 April 2009. The objective was to identify the different conceptual approaches to adherence research. Definitions were analyzed according to time and methodological perspectives. A taxonomic approach was subsequently derived, evaluated and discussed with international experts. More than 10 different terms describing medication‐taking behaviour were identified through the literature review, often with differing meanings. The conceptual foundation for a new, transparent taxonomy relies on three elements, which make a clear distinction between processes that describe actions through established routines (“Adherence to medications”, “Management of adherence”) and the discipline that studies those processes (“Adherence‐related sciences”). “Adherence to medications” is the process by which patients take their medication as prescribed, further divided into three quantifiable phases: “Initiation”, “Implementation” and “Discontinuation”. In response to the proliferation of ambiguous or unquantifiable terms in the literature on medication adherence, this research has resulted in a new conceptual foundation for a transparent taxonomy. The terms and definitions are focused on promoting consistency and quantification in terminology and methods to aid in the conduct, analysis and interpretation of scientific studies of medication adherence.", + "accessed": { + "date-parts": [ + [ + 2025, + 4, + 22 + ] + ] + }, + "author": [ + { + "family": "Vrijens", + "given": "Bernard" + }, + { + "family": "De Geest", + "given": "Sabina" + }, + { + "family": "Hughes", + "given": "Dyfrig A." + }, + { + "family": "Przemyslaw", + "given": "Kardas" + }, + { + "family": "Demonceau", + "given": "Jenny" + }, + { + "family": "Ruppar", + "given": "Todd" + }, + { + "family": "Dobbels", + "given": "Fabienne" + }, + { + "family": "Fargher", + "given": "Emily" + }, + { + "family": "Morrison", + "given": "Valerie" + }, + { + "family": "Lewek", + "given": "Pawel" + }, + { + "family": "Matyjaszczyk", + "given": "Michal" + }, + { + "family": "Mshelia", + "given": "Comfort" + }, + { + "family": "Clyne", + "given": "Wendy" + }, + { + "family": "Aronson", + "given": "Jeffrey K." + }, + { + "family": "Urquhart", + "given": "J." + }, + { + "literal": "for the ABC Project Team" + } + ], + "container-title": "British Journal of Clinical Pharmacology", + "id": "vrijens_new_2012", + "issue": "5", + "issued": { + "date-parts": [ + [ + 2012, + 5 + ] + ] + }, + "page": "691-705", + "title": "A new taxonomy for describing and defining adherence to medications", + "type": "article-journal", + "volume": "73" + }, + { + "DOI": "10.1542/peds.2011-1635", + "ISSN": "0031-4005, 1098-4275", + "URL": "https://publications.aap.org/pediatrics/article/129/2/e473/32549/Pediatric-Self-management-A-Framework-for-Research", + "abstract": "Self-management of chronic pediatric conditions is a formidable challenge for patients, families, and clinicians, with research demonstrating a high prevalence of poor self-management and nonadherence across pediatric conditions. Nevertheless, effective self-management is necessary to maximize treatment efficacy and clinical outcomes and to reduce unnecessary health care utilization and costs. However, this complex behavior is poorly understood as a result of insufficient definitions, reliance on condition-specific and/or adult models of self-management, failure to consider the multitude of factors that influence patient self-management behavior, and lack of synthesis of research, clinical practice, and policy implications. To address this need, we present a comprehensive conceptual model of pediatric self-management that articulates the individual, family, community, and health care system level influences that impact self-management behavior through cognitive, emotional, and social processes. This model further describes the relationship among self-management, adherence, and outcomes at both the patient and system level. Implications for research, clinical practice, and health care policy concerning pediatric chronic care are emphasized with a particular focus on modifiable influences, evidence-based targets for intervention, and the role of clinicians in the provision of self-management support. We anticipate that this unified conceptual approach will equip stakeholders in pediatric health care to (1) develop evidence-based interventions to improve self-management, (2) design programs aimed at preventing the development of poor self-management behaviors, and (3) inform health care policy that will ultimately improve the health and psychosocial outcomes of children with chronic conditions.", + "accessed": { + "date-parts": [ + [ + 2025, + 4, + 22 + ] + ] + }, + "author": [ + { + "family": "Modi", + "given": "Avani C." + }, + { + "family": "Pai", + "given": "Ahna L." + }, + { + "family": "Hommel", + "given": "Kevin A." + }, + { + "family": "Hood", + "given": "Korey K." + }, + { + "family": "Cortina", + "given": "Sandra" + }, + { + "family": "Hilliard", + "given": "Marisa E." + }, + { + "family": "Guilfoyle", + "given": "Shanna M." + }, + { + "family": "Gray", + "given": "Wendy N." + }, + { + "family": "Drotar", + "given": "Dennis" + } + ], + "container-title": "Pediatrics", + "id": "modi_pediatric_2012", + "issue": "2", + "issued": { + "date-parts": [ + [ + 2012, + 2 + ] + ] + }, + "page": "e473-e485", + "title": "Pediatric Self-management: A Framework for Research, Practice, and Policy", + "title-short": "Pediatric Self-management", + "type": "article-journal", + "volume": "129" + }, + { + "URL": "https://www.R-project.org/", + "author": [ + { + "literal": "R Core Team" + } + ], + "id": "R-base", + "issued": { + "date-parts": [ + [ + 2020 + ] + ] + }, + "publisher": "R Foundation for Statistical Computing", + "publisher-place": "Vienna, Austria", + "title": "R: A language and environment for statistical computing", + "title-short": "R", + "type": "book" + }, + { + "author": [ + { + "family": "Xie", + "given": "Yihui" + } + ], + "id": "R-bookdown", + "issued": { + "date-parts": [ + [ + 2021 + ] + ] + }, + "note": "https://github.com/rstudio/bookdown, https://pkgs.rstudio.com/bookdown/", + "title": "Bookdown: Authoring books and technical documents with r markdown", + "title-short": "Bookdown", + "type": "book" + }, + { + "URL": "https://yihui.org/knitr/", + "author": [ + { + "family": "Xie", + "given": "Yihui" + } + ], + "id": "R-knitr", + "issued": { + "date-parts": [ + [ + 2021 + ] + ] + }, + "note": "R package version 1.33", + "title": "Knitr: A general-purpose package for dynamic report generation in r", + "title-short": "Knitr", + "type": "book" + }, + { + "author": [ + { + "family": "Allaire", + "given": "JJ" + }, + { + "family": "Xie", + "given": "Yihui" + }, + { + "family": "McPherson", + "given": "Jonathan" + }, + { + "family": "Luraschi", + "given": "Javier" + }, + { + "family": "Ushey", + "given": "Kevin" + }, + { + "family": "Atkins", + "given": "Aron" + }, + { + "family": "Wickham", + "given": "Hadley" + }, + { + "family": "Cheng", + "given": "Joe" + }, + { + "family": "Chang", + "given": "Winston" + }, + { + "family": "Iannone", + "given": "Richard" + } + ], + "id": "R-rmarkdown", + "issued": { + "date-parts": [ + [ + 2021 + ] + ] + }, + "note": "https://github.com/rstudio/rmarkdown, https://pkgs.rstudio.com/rmarkdown/", + "title": "Rmarkdown: Dynamic documents for r", + "title-short": "Rmarkdown", + "type": "book" + }, + { + "URL": "https://bookdown.org/yihui/bookdown", + "author": [ + { + "family": "Xie", + "given": "Yihui" + } + ], + "id": "bookdown2016", + "issued": { + "date-parts": [ + [ + 2016 + ] + ] + }, + "note": "ISBN 978-1138700109", + "publisher": "Chapman; Hall/CRC", + "publisher-place": "Boca Raton, Florida", + "title": "Bookdown: Authoring books and technical documents with R markdown", + "title-short": "Bookdown", + "type": "book" + }, + { + "URL": "https://yihui.org/knitr/", + "author": [ + { + "family": "Xie", + "given": "Yihui" + } + ], + "edition": "2nd", + "id": "knitr2015", + "issued": { + "date-parts": [ + [ + 2015 + ] + ] + }, + "note": "ISBN 978-1498716963", + "publisher": "Chapman; Hall/CRC", + "publisher-place": "Boca Raton, Florida", + "title": "Dynamic documents with R and knitr", + "type": "book" + }, + { + "URL": "http://www.crcpress.com/product/isbn/9781466561595", + "author": [ + { + "family": "Xie", + "given": "Yihui" + } + ], + "container-title": "Implementing reproducible computational research", + "editor": [ + { + "family": "Stodden", + "given": "Victoria" + }, + { + "family": "Leisch", + "given": "Friedrich" + }, + { + "family": "Peng", + "given": "Roger D." + } + ], + "id": "knitr2014", + "issued": { + "date-parts": [ + [ + 2014 + ] + ] + }, + "note": "ISBN 978-1466561595", + "publisher": "Chapman; Hall/CRC", + "title": "Knitr: A comprehensive tool for reproducible research in R", + "title-short": "Knitr", + "type": "chapter" + }, + { + "URL": "https://bookdown.org/yihui/rmarkdown", + "author": [ + { + "family": "Xie", + "given": "Yihui" + }, + { + "family": "Allaire", + "given": "J. J." + }, + { + "family": "Grolemund", + "given": "Garrett" + } + ], + "id": "rmarkdown2018", + "issued": { + "date-parts": [ + [ + 2018 + ] + ] + }, + "note": "ISBN 9781138359338", + "publisher": "Chapman; Hall/CRC", + "publisher-place": "Boca Raton, Florida", + "title": "R markdown: The definitive guide", + "title-short": "R markdown", + "type": "book" + }, + { + "URL": "https://bookdown.org/yihui/rmarkdown-cookbook", + "author": [ + { + "family": "Xie", + "given": "Yihui" + }, + { + "family": "Dervieux", + "given": "Christophe" + }, + { + "family": "Riederer", + "given": "Emily" + } + ], + "id": "rmarkdown2020", + "issued": { + "date-parts": [ + [ + 2020 + ] + ] + }, + "note": "ISBN 9780367563837", + "publisher": "Chapman; Hall/CRC", + "publisher-place": "Boca Raton, Florida", + "title": "R markdown cookbook", + "type": "book" + } + ], + "project_biblios": [ + "book.bib", + "packages.bib" + ] +} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/00-intro.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/00-intro.Rmd.xref new file mode 100644 index 0000000..590d6a4 --- /dev/null +++ b/.Rproj.user/6F8C099B/bookdown-crossref/00-intro.Rmd.xref @@ -0,0 +1,5 @@ +h1:introduction Introduction +h2:motivation Motivation +h2:topics-covered Topics Covered +h2:curriculum Curriculum + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/01-data_types.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/01-data_types.Rmd.xref new file mode 100644 index 0000000..64429a4 --- /dev/null +++ b/.Rproj.user/6F8C099B/bookdown-crossref/01-data_types.Rmd.xref @@ -0,0 +1,11 @@ +h1:clinical-data-types Clinical Data Types +h2:learning-objectives Learning Objectives +h2:clinical-data-is-unique Clinical data is unique +h2:major-clinical-data-types Major clinical data types +h3:structured-data Structured data +h3:unstructured-data-clinical-notes Unstructured data / clinical notes +h2:description-of-data Description of data +h3:file-types File types +h3:metadata Metadata +h2:summary Summary + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/01b-specific_data_types.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/01b-specific_data_types.Rmd.xref new file mode 100644 index 0000000..4899754 --- /dev/null +++ b/.Rproj.user/6F8C099B/bookdown-crossref/01b-specific_data_types.Rmd.xref @@ -0,0 +1,12 @@ +h1:specific-clinical-data-types Specific Clinical Data Types +h2:learning-objectives Learning Objectives +h2:physiological Physiological +h2:monitoring-data Monitoring data +h3:benefits-of-monitoring-data Benefits of Monitoring Data +h3:considerations Considerations +h4:medication-adherence Medication Adherence +h2:radiology Radiology +h2:pathology Pathology +h2:synthetic-data Synthetic Data +h2:summary Summary + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/01c-how_to_collect_data.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/01c-how_to_collect_data.Rmd.xref new file mode 100644 index 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Secondary Sources +h2:retrospective-analyses Retrospective Analyses +h2:summary Summary + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/02-data_uses.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/02-data_uses.Rmd.xref new file mode 100644 index 0000000..be005b8 --- /dev/null +++ b/.Rproj.user/6F8C099B/bookdown-crossref/02-data_uses.Rmd.xref @@ -0,0 +1,12 @@ +h1:clinical-data-uses Clinical Data Uses +h2:learning-objectives Learning Objectives +h2:general-uses-of-clinical-data General uses of clinical data +h3:cancer-prevention-and-care Cancer Prevention and Care +h2:types-of-questions-that-can-be-asked-with-clinical-data Types of questions that can be asked with clinical data +h3:risk-prediction Risk Prediction +h3:cohort-identification-for-research Cohort identification for research +h3:clinical-studies-and-trials Clinical studies and trials +h3:retrospective-analysis Retrospective analysis +h2:research-design-considerations Research Design Considerations +h2:conclusion Conclusion + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/03-data_management.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/03-data_management.Rmd.xref new file mode 100644 index 0000000..4b31a99 --- /dev/null +++ b/.Rproj.user/6F8C099B/bookdown-crossref/03-data_management.Rmd.xref @@ -0,0 +1,8 @@ +h1:clinical-data-management Clinical Data Management +h2:learning-objectives Learning Objectives +h2:clinical-data-handling-tools Clinical data handling tools +h2:importance-of-keeping-clinical-data-safe Importance of keeping clinical data safe +h2:government-regulators Government regulators +h2:documentation Documentation +h2:conclusion Conclusion + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/04-appendixI.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/04-appendixI.Rmd.xref new file mode 100644 index 0000000..0447db7 --- /dev/null +++ b/.Rproj.user/6F8C099B/bookdown-crossref/04-appendixI.Rmd.xref @@ -0,0 +1,8 @@ +h1:appendix-i-ontologies-vocabularies-taxonomies-and-standards Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards +h2:snomed SNOMED +h2:icd10 ICD10 +h2:rxnorm RxNorm +h2:loinc LOINC +h2:emerging-standards-such-as-fhir Emerging standards such as FHIR +h2:omop OMOP + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/About.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/About.Rmd.xref new file mode 100644 index 0000000..4d55ec0 --- /dev/null +++ b/.Rproj.user/6F8C099B/bookdown-crossref/About.Rmd.xref @@ -0,0 +1,2 @@ +h1:about-the-authors About the Authors + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/References.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/References.Rmd.xref new file mode 100644 index 0000000..d24a95a --- /dev/null +++ b/.Rproj.user/6F8C099B/bookdown-crossref/References.Rmd.xref @@ -0,0 +1,2 @@ +h1:references References + diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/index-itcr.Rmd.xref 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b/.Rproj.user/6F8C099B/sources/session-87f78f51/26811BE8-contents new file mode 100644 index 0000000..33a998f --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/26811BE8-contents @@ -0,0 +1,23 @@ +--- +title: "Informatics Technology for Cancer Research (ITCR): Course Name " +date: "`r format(Sys.time(), '%B, %Y')`" +site: bookdown::bookdown_site +documentclass: book +bibliography: [book.bib, packages.bib] +biblio-style: apalike +link-citations: yes +description: "Description about Course/Book." +favicon: assets/ITN_favicon.ico +--- + + +```{r include=FALSE} +# automatically create a bib database for R packages +knitr::write_bib(c( + .packages(), "bookdown", "knitr", "rmarkdown" +), "packages.bib") +``` + +# About this Course {-} + +This course is part of a series of courses for the [Informatics Technology for Cancer Research (ITCR)](https://itcr.cancer.gov/) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR Training Network (ITN) which is a collaborative effort of researchers around the United States to support cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: [National Cancer Institute (NCI)](https://www.cancer.gov/) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our website at [www.itcrtraining.org](www.itcrtraining.org) for more information. diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/3E83D136-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/3E83D136-contents new file mode 100644 index 0000000..ad7e3b4 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/3E83D136-contents @@ -0,0 +1,551 @@ +@import url('https://fonts.googleapis.com/css?family=Abril+Fatface|Karla:400,400i,700,700i|Lora:400,400i,700,700i&display=swap'); + +p.caption { + color: #777; + margin-top: 10px; +} +p code { + white-space: inherit; +} +pre { + word-break: normal; + word-wrap: normal; +} +pre code { + white-space: inherit; +} + +/*---------------Hero Image------------------*/ + +/* next 3 rules are for setting large image at top of each page and pushing down the book content to appear beneath the image */ + +.hero-image-container { + position: absolute; + top: 0; + left: 0; + right: 0; + height: 200px; + /*background-image: url("images/books.jpg");*/ + /*background-color: #193a5c;*/ +} + +.hero-image { + width: 100%; + height: 80%; + /*object-fit: cover;*/ +} + +.page-inner { + padding-top: 140px !important; +} + + +/* ------------Links------------------ */ + +.book .book-body .page-wrapper .page-inner section.normal a { + color: #e0471c; +} + + +/*------------- Body and header text---------------- */ + +.book.font-family-1 { + font-family: 'Karla', arial, sans-serif; +} + +h1, h2, h3, h4 { + font-family: 'Lora', arial, sans-serif; +} + + +.book .book-body .page-wrapper .page-inner section.normal h1, +.book .book-body .page-wrapper .page-inner section.normal h2, +.book .book-body .page-wrapper .page-inner section.normal h3, +.book .book-body .page-wrapper .page-inner section.normal h4, +.book .book-body .page-wrapper .page-inner section.normal h5, +.book .book-body .page-wrapper .page-inner section.normal h6 { + margin-top: 2.275em; + margin-bottom: 1em; +} + +.title { + font-family: 'Lora'; + font-size: 4em !important; + color: #193a5c; + margin-top: 0.275em !important; + margin-bottom: 0.35em !important; +} + +.subtitle { + font-family: 'Lora'; + color: #0b8d96; +} + + +/*----------DROP CAPS--------------*/ + + +/*p:nth-child(2):first-letter { /* /* DROP-CAP FOR FIRST P BENEATH EACH H1 OR H2*/ /* + color: #3fb5bd; + float: left; + font-family: 'Abril Fatface', serif; + font-size: 7em; + line-height: 65px; + padding-top: 4px; + padding-right: 8px; + padding-left: 3px; + margin-bottom: 9px; +} +*/ + +.section.level1 > p:first-of-type:first-letter { /*drop cap for first p beneath level 1 headers only within class .section*/ + color: #193a5c; + float: left; + font-family: 'Abril Fatface', serif; + font-size: 7em; + line-height: 65px; + padding-top: 4px; + padding-right: 8px; + padding-left: 3px; + margin-bottom: 9px; +} + +/* add drop cap to first paragraph that follows the first 2nd level header*/ +/* +.section.level2:first-of-type > p:first-of-type:first-letter { + color: #3fb5bd; + float: left; + font-family: 'Abril Fatface', serif; + font-size: 7em; + line-height: 65px; + padding-top: 4px; + padding-right: 8px; + padding-left: 3px; + margin-bottom: 9px; +} +*/ + + + +/*------------ TOC --------------*/ + + +.book .book-summary { + background: white; + border-right: none; +} + +/*---color of links in TOC----*/ + +.book .book-summary a { +color: #193a5c +} + +.summary{ + font-family: 'Karla', sans-serif; +} + +/* all TOC list items, basically */ +.book .book-summary ul.summary li a, +.book .book-summary ul.summary li span { + padding-top: 8px; + padding-bottom: 8px; + padding-left: 15px; + padding-right: 15px; + color: #193a5c; +} + +.summary a:hover { + color: #e0471c !important; +} + +.book .book-summary ul.summary li.active>a { /*active TOC links*/ + color: #e0471c !important; + border-left: solid 4px; + border-color: #e0471c; + padding-left: 11px !important; +} + +.trapezoid { + width: 130px; + text-align: center; + height: 15; + position: relative; + border-right: 50px solid transparent; + border-bottom: 50px solid #dbdbdb; + border-left: 50px solid transparent; + box-sizing: content-box; +} +.trapezoid span { + position: absolute; + top: 5px; + bottom: 5px; + left: 5%; + color: #fff; +} + + + + +li.appendix span, li.part span { /* for TOC part names */ + margin-top: 1em; + color: #000000; + opacity: .9 !important; + text-transform: uppercase; +} + +.part + li[data-level=""] { /* grabs first .chapter immediately after .part...but only those ch without numbers */ + text-transform: uppercase; +} + + +/* The next two rules make the horizontal line go straight across in top navbar */ + +.summary > li:first-child { + height: 50px; + padding-top: 10px; + border-bottom: 1px solid rgba(0,0,0,.07); +} + +.book .book-summary ul.summary li.divider { + height: 0px; +} + + + +/*--- LOGO ---*/ + +.toc-logo { + width: 200px !important; + object-fit: contain; + margin: 0 auto; +} + +.toc-logo img { + max-width: 100%; + margin-bottom: 10px; +} + +.summary > li:first-child { + height: auto !important; +} + +/* --------------Two columns--------------- */ + +.col2 { + columns: 2 200px; /* number of columns and width in pixels*/ + -webkit-columns: 2 200px; /* chrome, safari */ + -moz-columns: 2 200px; /* firefox */ +} + + +.side-by-side { + display: flex; +} + +.side1 { + width: 40%; +} + +.side2 { + width: 58%; + margin-left: 1rem; +} + +/* --------------Even split- two columns--------------- */ + +.split { + display: flex; +} + +.split1 { + width: 50%; + padding-right: 20px; +} + +.split2 { + width: 50%; +} + + +/* Sidebar formating --------------------------------------------*/ +/* from r-pkgs.org*/ + +div.notice{ + border: 4px #193a5c; + border-style: solid; + padding: 1em; + margin: 1em 0; + padding-left: 100px; + background-size: 70px; + background-repeat: no-repeat; + background-position: 15px center; + min-height: 120px; + background-color: #e8ebee; +} + +div.notice{ + background-image: url("../assets/box_images/note.png"); +} + +div.warning{ + border: 4px #e0471c; + border-style: solid; + padding: 1em; + margin: 1em 0; + padding-left: 100px; + background-size: 70px; + background-repeat: no-repeat; + background-position: 15px center; + min-height: 120px; + background-color: #e8ebee; +} + +div.warning{ + background-image: url("../assets/box_images/warning.png"); +} + + + +div.github{ + border: 4px #000000; + border-style: solid; + padding: 1em; + margin: 1em 0; + padding-left: 100px; + background-size: 70px; + background-repeat: no-repeat; + background-position: 15px center; + min-height: 120px; + background-color: #e8ebee; +} + +div.github{ + background-image: url("../assets/box_images/github.png"); +} + +div.dictionary{ + border: 4px #193a5c; + border-style: solid; + padding: 1em; + margin: 1em 0; + padding-left: 100px; + background-size: 70px; + background-repeat: no-repeat; + background-position: 15px center; + min-height: 120px; + background-color: #e8ebee; +} + +div.dictionary{ + background-image: url("../assets/box_images/dictionary.png"); +} + +div.reflection{ + border: 4px #193a5c; + border-style: solid; + padding: 1em; + margin: 1em 0; + padding-left: 100px; + background-size: 90px; + background-repeat: no-repeat; + background-position: 15px center; + min-height: 120px; + background-color: #e8ebee; +} + +div.reflection{ + background-image: url("../assets/box_images/thinking_face.png"); +} + + +div.rstudio-tip, div.tip, div.gotcha, div.design, div.hat{ + border: 4px #aed6d963; + border-style: dashed solid; + padding: 1em; + margin: 1em 0; + padding-left: 100px; + background-size: 70px; + background-repeat: no-repeat; + background-position: 15px center; + min-height: 120px; + color: #2ea8b3; + background-color: #fcfcfc; +} + + +div.rstudio-tip { + background-image: url("../images/divs/rstudio.png"); +} + +div.tip { + background-image: url("../images/divs/lightbulb.png"); +} + +div.gotcha { + background-image: url("../images/divs/gotcha_2.png"); +} + +div.design { + background-image: url("../images/divs/design.png"); +} + +div.hat { + background-image: url("../images/divs/hat.png"); +} + +/* for fancy bookdown cookbook */ + +div.puzzle, div.fyi, div.demo, div.note { + padding: 1em; + margin: 1em 0; + padding-left: 100px; + background-size: 70px; + background-repeat: no-repeat; + background-position: 15px center; + min-height: 120px; + color: #1f5386; + background-color: #bed3ec; + border: solid 5px #dfedff; +} + +div.puzzle { + background-image: url("../images/illos/Your-turn.png"); +} + +div.fyi { + background-image: url("../images/illos/fyi.png"); +} + +div.demo { + background-image: url("../images/illos/Live-code.png"); +} + +div.note { + background-image: url("../images/illos/lightbulb2.png"); +} + + +/* .book .book-body .page-wrapper .page-inner section.normal is needed + to override the styles produced by gitbook, which are ridiculously + overspecified. Goal of the selectors is to ensure internal "margins" + controlled only by padding of container */ + +.book .book-body .page-wrapper .page-inner section.normal div.rstudio-tip > :first-child, +.book .book-body .page-wrapper .page-inner section.normal div.tip > :first-child { + margin-top: 0; +} + +.book .book-body .page-wrapper .page-inner section.normal div.rstudio-tip > :last-child, +.book .book-body .page-wrapper .page-inner section.normal div.tip > :last-child { + margin-bottom: 0; +} + +iframe { + -moz-transform-origin: top left; + -webkit-transform-origin: top left; + -o-transform-origin: top left; + -ms-transform-origin: top left; + transform-origin: top left; +} + +.iframe-container { + overflow: auto; + -webkit-overflow-scrolling: touch; + border: #ddd 2px solid; + box-shadow: #888 0px 5px 8px; + margin-bottom: 1em; +} + +.iframe-container > iframe { + border: none; +} + + +/* -------Clipboard copy button --------- */ +.copy { + width: inherit; + background-color: #e2e2e2 ; + border: none; + border-radius: 2px; + float: right; + font-size: 60%; + padding: 4px 4px 4px 4px; +} + +/* for new copy button */ + +.hasCopyButton { + position: relative; +} + +.btn-copy-ex { + position: absolute; + right: 0; + top: 0; + visibility: hidden; +} + +.hasCopyButton:hover button.btn-copy-ex { + visibility: visible; +} + + + +/* -----------Section anchors -------------*/ + +.book .book-body .page-wrapper .page-inner section.normal { + overflow: visible !important; /*so anchor link doesn't get cut off */ +} + +a.anchor { + margin-left: -30px; + padding-right: 3px; + display:inline-block; + width: 30px; + height: 30px; + /*visibility: hidden;*/ + background-image: url(https://image.flaticon.com/icons/svg/34/34735.svg) !important; + background-repeat: no-repeat !important; + background-size: 20px 20px !important; + background-position: center bottom !important; + opacity: 0; +} + + +.hasAnchor:hover a.anchor, +a.anchor:hover { + /*visibility: visible;*/ + opacity: 0.6; +} + +/* disable anchors for headers with "no-anchor" class */ +.no-anchor .hasAnchor:hover a.anchor, +.no-anchor a.anchor:hover { + opacity: 0 !important; +} + +@media (max-width: 1144px) { /* don't show anchor link on small screens */ + .hasAnchor:hover a.anchor { + visibility: hidden; + } +} + + +/*____Code Chunks____*/ + +.sourceCode { + margin-bottom: 0.85em; /* adds same amount of margin as a

would */ +} + +.sourceCode pre { + margin: 0 !important; /* makes space above and below symmetrical*/ +} + +/* Footer */ + +.footer { + font-family: "Lora", serif; + font-size: .85em; + color: #193a5c; +} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2 b/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2 new file mode 100644 index 0000000..e2f9692 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2 @@ -0,0 +1,26 @@ +{ + "id": "3F1840A2", + "path": "~/Documents/clinical_data_course/Clinical_Data_Management/01d-how_to_acquire_data.Rmd", + "project_path": "01d-how_to_acquire_data.Rmd", + "type": "r_markdown", + "hash": "0", + "contents": "", + "dirty": false, + "created": 1781803495658.0, + "source_on_save": false, + "relative_order": 4, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "14,55", + "scrollLine": "2" + }, + "folds": "", + "lastKnownWriteTime": 1781805265, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1781805265532, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2-contents new file mode 100644 index 0000000..74fe804 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2-contents @@ -0,0 +1,19 @@ +# How to Acquire Clinical Data + +## Learning Objectives + +## Electronic Health Records + +## Medical Databases (?) + +## Registries (?) + +## Secondary Sources + +## Retrospective Analyses + +Does this belong in collecting data or acquiring data? + +Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. + +## Summary diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89 b/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89 new file mode 100644 index 0000000..1b27c38 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89 @@ -0,0 +1,26 @@ +{ + "id": "6FE7FC89", + "path": "~/Documents/clinical_data_course/Clinical_Data_Management/01-data_types.Rmd", + "project_path": "01-data_types.Rmd", + "type": "r_markdown", + "hash": "3828264916", + "contents": "", + "dirty": false, + "created": 1781803614828.0, + "source_on_save": false, + "relative_order": 5, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "72,635", + "scrollLine": "69" + }, + "folds": "", + "lastKnownWriteTime": 1781803626, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1781803626629, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89-contents new file mode 100644 index 0000000..afa93af --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89-contents @@ -0,0 +1,82 @@ +--- +title: Clinical Data Types +--- + +```{r, include = FALSE} +ottrpal::set_knitr_image_path() +``` + + +# Clinical Data Types + +Clinical data is health-related information collected from patients throughout their healthcare journey. It may come in many forms and its sensitive nature requires careful management by researchers. + +## Learning Objectives + +```{r, fig.align='center', echo = FALSE, fig.alt= "Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data", out.width="100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g3385bea4ad0_0_30" ) +``` + + + +## Clinical data is unique + +Clinical data refers to information __collected from patients__ during healthcare delivery, clinical trials, and medical research. Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data with predictable and consistent formats, clinical data is often heterogeneous, integrating many forms of both structured and unstructured data: quantitative measurements, categorical data, genotyping, images, subjective narratives from patient notes, and objective observations or conclusions. The unstructured nature of free text from notes reflecting subjective patient experiences or qualitative insights from healthcare professionals especially adds a further layer of complexity. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data's unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare. Further, because clinical data contains sensitive, personal information about patients, there are additional security and ethics concerns in the handling and management of clinical data. + +## Major clinical data types + +Clinical data can come in many different forms, including + +* patient demographics +* medical history or records such as diagnoses, lab results, vital signs, medication records, or procedure history +* genetic reports +* health monitor data +* images +* scanned documents, and notes written by physicians, nurses, and other clinicians +* survey/ case report form (CRF responses) +* and more ... + +```{r fig.align='center', echo = FALSE, fig.alt= "Clinical data refers to information collected from patients during healthcare delivery, clinical trials, and medical research and could include demographics, medical history, lab results, imaging, treatment outcomes, and more", out.width="100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.g35bf5e18bfe_0_0#slide=id.g35bf5e18bfe_0_0") +``` + +Some sources of clinical data are more prevalent and readily obtainable than others. For instance, notes, demographics, images, and histories or observations/records (lab results, vitals, medications, procedures) are often stored directly in electronic medical record systems making them more easily accessible and so are the focus of most Electronic Health Record (EHR) data research efforts. + +### Structured data + +Observational records such as test results and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Structured data types frequently used in EHR research consist of comprehensive longitudinal records of a patient's interactions with a healthcare system and may include demographics, diagnoses, lab values, procedures, vitals, and medication records. + +```{r, fig.align='center', echo = FALSE, fig.alt= "Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. ", out.width="100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g338f75828af_0_7" ) +``` + +The structured nature of this data allows for it to be stored in tables which may be indexed by a patient or visit ID and often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. + +Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or "billing codes", provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. + +```{r, fig.align='center', echo = FALSE, fig.alt= "Example of structured data, a table that includes patient id numbers, billing codes, dates, blood pressure measurements, weight measurements, height measurements, and prescribed medications coded using the National Drug Code (NDC)", out.width="100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g3385bea4ad0_0_0" ) +``` + +As we've described it, structured clinical data is expected to have similarities, although specific details may vary based on the type of EHR being used and any customizations to the EHR for the specific environment or institution in which data was collected (e.g., any specialized pre-processing by institutional research offices prior to providing data to researchers). So it is important for researchers to consider differences present in their data if they've obtained it from multiple contexts or institutions. + +### Unstructured data / clinical notes + +Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. + + +```{r, fig.align='center', echo = FALSE, fig.alt= "Unstructured Data - Data without specific format: includes images, pathology reports, radiology reports, clinical notes, discharge summaries and more. Includes an image of an x-ray and some clinical notes that states: Patient reports a sharp pain in the right lower abdomen for the past 24 hours.Temperature 98.6°F, BP 120/80, tenderness in the right lower quadrant. Suspected appendicitis. Refer to surgery for evaluation, initiate IV fluids, and pain management.", out.width="100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g3385bea4ad0_0_14" ) +``` + + +When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [@Dhir2008] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. + +## Description of data + +### File types + +### Metadata + + +## Summary diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/71A103E4-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/71A103E4-contents new file mode 100644 index 0000000..011f3e2 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/71A103E4-contents @@ -0,0 +1,21 @@ +--- +title: Clinical Data Management +--- + +```{r, include = FALSE} +ottrpal::set_knitr_image_path() +``` + +# Clinical Data Management + +## Learning Objectives + +## Clinical data handling tools + +## Importance of keeping clinical data safe + +## Government regulators + +## Documentation + +## Conclusion \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF b/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF new file mode 100644 index 0000000..3b82548 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF @@ -0,0 +1,26 @@ +{ + "id": "7D432FCF", + "path": "~/Documents/clinical_data_course/Clinical_Data_Management/_bookdown.yml", + "project_path": "_bookdown.yml", + "type": "yaml", + "hash": "3764396951", + "contents": "", + "dirty": false, + "created": 1781803327810.0, + "source_on_save": false, + "relative_order": 2, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "8,40", + "scrollLine": "0" + }, + "folds": "", + "lastKnownWriteTime": 1781803435, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1781803435517, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF-contents new file mode 100644 index 0000000..9eb9ae1 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF-contents @@ -0,0 +1,21 @@ +book_filename: "Course_Name" +chapter_name: "Chapter " +repo: https://github.com/jhudsl/DaSL_Course_Template_Bookdown/ +rmd_files: ["index.Rmd", + "00-intro.Rmd", + "01-data_types.Rmd", + "01b-specific_data_types.Rmd", + "01c-how_to_collect_data.Rmd", + "01d-how_to_acquire_data.Rmd" + "02-data_uses.Rmd", + "03-data_management.Rmd", + "04-appendixI.Rmd", + "About.Rmd", + "References.Rmd"] +new_session: yes +bibliography: [book.bib, packages.bib] +delete_merged_file: true +language: + ui: + chapter_name: "Chapter" +output_dir: "docs" diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF b/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF new file mode 100644 index 0000000..c58b69a --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF @@ -0,0 +1,26 @@ +{ + "id": "7DF61ECF", + "path": "~/Documents/clinical_data_course/Clinical_Data_Management/00-intro.Rmd", + "project_path": "00-intro.Rmd", + "type": "r_markdown", + "hash": "0", + "contents": "", + "dirty": false, + "created": 1781803180451.0, + "source_on_save": false, + "relative_order": 1, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "14,0", + "scrollLine": "0" + }, + "folds": "", + "lastKnownWriteTime": 1781029074, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1781029074, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF-contents new file mode 100644 index 0000000..aca5a2d --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF-contents @@ -0,0 +1,44 @@ +--- +title: "Clinical Data Management" +--- + + +```{r, include = FALSE} +ottrpal::set_knitr_image_path() +``` + +# Introduction + +```{r, fig.align='center', echo = FALSE, fig.alt= "Clinical Data Management, Written and assembled by: Kate Isaac & Carrie Wright, Contributors: Christopher Battiston, David Hanauer, Guilherme Del Fiol, Harry Hochheiser, Jennifer Kelleher, Abby Robbertz, & Meghan McGrady", out.width= "100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.g34e03a999ff_0_165#slide=id.g34e03a999ff_0_165") +``` + + +## Motivation + + +This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines. + +This course is intended to provide an overview of the different kinds of data commonly used in clinical research, the questions that can typically be asked with such data, as well as guidance for how to manage this data. + + +```{r for_individuals_who, fig.align='center', echo = FALSE, fig.alt= "For individuals who: Have clinical data and don’t know what to do with it. Want a basic overview of what the clinical data represent and what questions can be asked.Need to know the basics of how to manage clinical data properly", out.width= "100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.g116525eff64_0_96#slide=id.g116525eff64_0_96") +``` + + +## Topics Covered + +```{r topics_covered, fig.align='center', echo = FALSE, fig.alt= "Concepts discussed in Clinical Data course: What does your clinical data type represent? How do you handle the clinical data securely and ethically? What types of analysis questions can one ask when it comes to different kinds of clinical data? Find resources, tools and tutorials to help you process and work with your data", out.width= "100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.g11db7c97851_0_143#slide=id.g11db7c97851_0_143") +``` + + +## Curriculum + +The course will cover key underlying principles and concepts in ethical data handling. + +```{r learning_objectives, fig.align='center', echo = FALSE, fig.alt= "Overall Course Learning Objectives. This course will demonstrate how to: List common sources and types of clinical data, Explain the unique aspects related to clinical data, Understand uses and the questions that can be asked with the major types of clinical data,Recognize major tools and methods for managing clinical data ethically and securely", out.width= "100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.gd422c5de97_0_10#slide=id.gd422c5de97_0_10") +``` + diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/A364B9F6-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/A364B9F6-contents new file mode 100644 index 0000000..dc9bf90 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/A364B9F6-contents @@ -0,0 +1,29 @@ +--- +title: "Untitled" +output: html_document +date: "2026-06-18" +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +## R Markdown + +This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . + +When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this: + +```{r cars} +summary(cars) +``` + +## Including Plots + +You can also embed plots, for example: + +```{r pressure, echo=FALSE} +plot(pressure) +``` + +Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot. diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36 b/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36 new file mode 100644 index 0000000..48e5340 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36 @@ -0,0 +1,26 @@ +{ + "id": "B3737D36", + "path": "~/Documents/clinical_data_course/Clinical_Data_Management/01c-how_to_collect_data.Rmd", + "project_path": "01c-how_to_collect_data.Rmd", + "type": "r_markdown", + "hash": "0", + "contents": "", + "dirty": false, + "created": 1781803494411.0, + "source_on_save": false, + "relative_order": 3, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "6,850", + "scrollLine": "0" + }, + "folds": "", + "lastKnownWriteTime": 1781805287, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1781805287104, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36-contents new file mode 100644 index 0000000..ea0f654 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36-contents @@ -0,0 +1,144 @@ +# How to Collect Clinical Data + +## Learning Objectives + +## Case Report Forms (CRFs) + +In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter -- specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). + +Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on **accessibility, sensitivity, specificity, and comfort for people** + +::: {.notice} + +While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren't necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions -- the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. + +::: + +Here, we explore the different categories of questions that may be included within case report forms: + +1. **Demographic and Socioeconomic Questions** + +Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. + +- **Sex and Gender**: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. +- **Ethnicity and Race**: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. +- **Socioeconomic Status**: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, "What is your current employment status?" with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. + +It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. + +2. **Health and Medial History Questions** + +Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. + +- **Medical Conditions and History**: Questions about past and present health conditions should use clear, accessible language. For example, "Have you ever been diagnosed with any of the following conditions? (Please check all that apply)" followed by a comprehensive list containing all necessary options. +- **Medication Use**: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. +- **Disability and Functional Status**: For many populations, it is important to use person-first language, such as "Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?" and provide space for participants to describe their specific needs. However, different populations have different preferences. The "Disability Language Style Guide" from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic [@ncdj_style_guide]. + +Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. + +3. **Experience and Quality of Life Questions** + +Understanding how health conditions and treatments affect participants' daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. + +- **Daily Living and Social Functioning**: Questions like "How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?" can help assess the impact on daily life, with options ranging from "never" to "always." +- **Emotional and Psychological Well-Being**: Including questions such as "In the past week, how often have you felt anxious or depressed?" using a scale from "not at all" to "very often" can provide insights into mental health needs. +- **Support Systems and Social Networks**: Asking about social support (e.g., "Do you have someone you can rely on for emotional support?") can help identify participants' needs for social and emotional resources. + +Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. + +4. **Sexual and Reproductive Health Questions** + +Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. + +- **Sexual Orientation and Gender Identity (SOGI)**: Instead of just predefined categories, open-ended questions like "How would you describe your sexual orientation?" and "What is your gender identity?" allow participants to self-identify. Offering the option to skip these questions respects participants' privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. +- **Reproductive Health**: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, "Are you currently using any form of contraception? If yes, please specify." +- **Sexual Activity and History**: Questions should be direct but framed sensitively, such as "Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs." + +These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. + +5. **Treatment Preferences and Decision-Making Questions** + +Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. + +- **Decision-Making Preferences**: Questions like "How involved would you like to be in decisions about your healthcare?" offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. +- **Cultural and Religious Considerations**: Asking, "Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?" ensures that care is respectful and culturally appropriate. +- **Treatment Burden**: Questions such as "What level of inconvenience or side effects would be acceptable to you when considering a treatment?" help to gauge participants' preferences and comfort levels. + +These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. + +6. **Accessibility and Accommodation Needs Questions** + +To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. + +- **Language and Communication Needs**: "What is your preferred language for communication? Do you need an interpreter or translated materials?" These questions help ensure that participants can understand the materials. +- **Physical Accessibility**: Asking, "Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?" ensures physical accessibility. +- **Format Preferences**: "Would you prefer to complete this form online, on paper, or verbally with assistance?" helps accommodate different needs and preferences. +Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. + +Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. + +7. **Cultural Sensitivity and Identity Questions** + +CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. + +- **Cultural Identity and Practices**: An open-ended question such as "Are there any cultural practices or beliefs that are important for us to be aware of in your care?" allows participants to share relevant information. +- **Dietary Restrictions and Preferences**: Asking, "Do you have any dietary restrictions or preferences that are culturally or religiously motivated?" ensures that these are respected. +- **Community and Belonging**: "Is there anything about your community or background that you would like us to know to provide better care?" encourages participants to share relevant aspects of their identity. + +These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. + +8. **Ensuring Comfort, Trust, and Privacy Questions** + +Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. + +- **Comfort and Confidentiality**: Asking, "Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?" helps build trust. +- **Feedback and Preferences**: "Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?" invites participants to share their feedback. +- **Consent and Voluntary Participation**: Questions like, "Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary." reinforce autonomy and respect. + +Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. + +::: {.warning} + +The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. + +::: + +Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. By thoughtfully addressing both unique study requirements and universally relevant data points, researchers can optimize CRFs for consistency across clinical studies. + +## Clinical Studies and Trials + +Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. + +Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. + +::: {.definition} +The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." +::: + +The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. + +::: {.definition} +**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. +::: + +## Research Design Considerations + +Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. + +One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, + +* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. +* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. +* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. +* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. + +Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: + +* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. +* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. +* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. +* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. + +With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. + +## Summary diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/C8C234F9-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/C8C234F9-contents new file mode 100644 index 0000000..4a308ea --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/C8C234F9-contents @@ -0,0 +1,77 @@ +# Specific Clinical Data Types + +## Learning Objectives + +```{r, fig.align='center', echo = FALSE, fig.alt= "Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data", out.width="100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g3385bea4ad0_0_30" ) +``` + + + +## Physiological + +## Monitoring data + +:::bluebox +This section was written by: Jennifer Kelleher, Ph.D.^1^; Abigail S. Robbertz, Ph.D.^1^; and Meghan E. McGrady, Ph.D.^1,2^ + +**NOTE:** Jennifer Kelleher, Ph.D.^1^ and Abigail S. Robbertz, Ph.D.^1^ contributed equally. + +^1^ Center for Adherence and Self-Management, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA + +^2^ Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA + +The work discussed in this section was also supported by the National Cancer Institute at the National Institutes of Health (R21CA263704, K07CA200668) to MEM. JK and ASR are supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development at the National Institutes of Health (T32HD068223). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. + +::: + +Electronic monitoring devices are digital tools that can be used to track health behaviors over time such as: + +* Sleep +* Physical activity +* Medication adherence +* Calorie intake + +Electronic monitoring devices can also be used to assess physical health indicators including: +* Blood glucose levels +* Blood pressure +* Heart rate and heart rate variability +* Oxygen saturation + +Electronic monitoring devices enable researchers to track day-to-day health behaviors in the patient’s **"real-world" setting**. This allows researchers to explore patterns or changes in a patient’s health behavior and provides a richer understanding of **daily behavior over time**. + + +### Benefits of Monitoring Data + +1) Electronic monitoring devices often include data transmission abilities that enable healthcare providers or researchers to access these data in near **real-time** potentially informing intervention and/or medical decision-making. + +1) Electronic monitoring devices also have the potential to produce **more accurate** estimates of health behaviors than alternative strategies (e.g., self-report) as they are not subject to recall bias and can detect efforts to inflate adherence due to social desirability. + +### Considerations + +:::warning +This section is not exhaustive. Research teams are strongly encouraged to consult with experts with experience and training in collecting and analyzing data from specific devices. + +To ensure the outcome variables are aligned with the research question of interest and [ethical and age/developmental considerations](https://pmc.ncbi.nlm.nih.gov/articles/PMC10798216/) (@psihogios_ethical_2024; @modi_pediatric_2012) have been appropriately accounted for, readers are encouraged to consult with researchers in their field who have integrated these measurement strategies into their work. +::: + +#### Medication Adherence + +There are three major components of medical adherence (the tracking of taking medication): + +- Initiation: Starting a prescribed regimen +- Implementation: The amount of which a patient's medication-taking behavior corresponds with the treatment regimen or protocol +- Discontinuation: Stopping a perscribed regimen + +For more information see: + +- [A new taxonomy for describing and defining adherence to medications (Vrijens et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC3403197/) +- [Pediatric self-management: A framework for research, practice, and policy. (Modi et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC9923567/) + +## Radiology + +## Pathology + +## Synthetic Data + +## Summary \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7 b/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7 new file mode 100644 index 0000000..993c88c --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7 @@ -0,0 +1,26 @@ +{ + "id": "DFF8DCC7", + "path": "~/Documents/clinical_data_course/Clinical_Data_Management/02-data_uses.Rmd", + "project_path": "02-data_uses.Rmd", + "type": "r_markdown", + "hash": "0", + "contents": "", + "dirty": false, + "created": 1781804963277.0, + "source_on_save": false, + "relative_order": 6, + "properties": { + "source_window_id": "", + "Source": "Source", + "cursorPosition": "295,135", + "scrollLine": "288" + }, + "folds": "", + "lastKnownWriteTime": 1781805267, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1781805267378, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7-contents new file mode 100644 index 0000000..d7dd267 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7-contents @@ -0,0 +1,298 @@ +--- +title: Clinical Data Uses +--- + +```{r, include = FALSE} +ottrpal::set_knitr_image_path() +``` + +# Clinical Data Uses + +## Learning Objectives + +## General uses of clinical data + +### Cancer Prevention and Care + +The near universal adoption of electronic health record (EHR) systems in the US has created unprecedented opportunities to improve cancer prevention and care. As described in previous chapters, EHR systems store comprehensive longitudinal records of a patient's interactions with a healthcare system, including data about: + + - demographics + - conditions + - family history + - clinical notes + - laboratory test results + - medications + - procedures + - imaging + - genetic test reports + + ```{r, fig.align='center', echo = FALSE, fig.alt= "Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. ", out.width="100%"} + ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g338f75828af_0_7" ) + ``` + +These data can be accessed not only by health professionals, but also by patients through patient portals. EHR data can also be used to enable data-driven interventions such as provider- and patient-facing clinical decision support (CDS) and population health management (PHM). + +CDS has been defined as tools that "provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care" [@Osheroff_2007]. Examples of widely adopted CDS tools with demonstrated effectiveness for cancer prevention, diagnosis, and care include: + + * provider and patient reminders for cancer screening + * decision aids and shared decision-making tools (e.g., for prostate cancer treatment, lung cancer screening) + * AI-supported diagnostic imaging + * chemotherapy decision support + * at-home symptom care + +While CDS tools generally provide decision support focused on a specific patient at a time, PHM are strategies that target specific patient populations [@Swarthout_Bishop_2017]. PHM efforts generally consist of: + + * population algorithms that are applied over EHR and other data sources to identify individuals who are eligible for a specific healthcare intervention (e.g., colorectal cancer screening, tobacco cessation, HPV vaccination) + * patient engagement strategies (e.g., patient portals, text messaging, chatbots, patient navigators) that provide education and access to those interventions + * analytic tools that assess the effectiveness of the PHM program. + +Several PHM programs have demonstrated to be effective in increasing the uptake of cancer prevention. For example, the colorectal cancer screening program at Kaiser Permanente uses digital (i.e., text messaging, patient portal), mailed, and patient navigation approaches to increase colorectal cancer screening by mailing Fecal Immunohistochemical Test (FIT) kits to patients' homes [@Gupta2020]. Also the Cancer Moonshot BRIDGE trial used the GARDE platform [@Bradshaw2022] (ITCR-funded) to identify candidates for genetic testing of hereditary cancer syndromes based on EHR data; and for patient outreach, pre- and post-test education via automated chatbots [@Kaphingst2024]. + + +```{r, fig.align='center', echo = FALSE, fig.alt= "Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM.", out.width="100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g338f75828af_0_28" ) +``` + +Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. + +While some CDS and PHM approaches have been successfully adopted widely, emerging technologies such as the use of generative AI approaches to analyze diagnostic imaging, large language models (LLMs) to extract information from narrative texts (e.g., clinical notes), LLM-based chatbots to communicate with patients, and digital health tools such as home-based sensors are creating unprecedented opportunities for next generation CDS and PHM. These approaches have the potential to enable significant breakthroughs through the implementation of patient-tailored cancer prevention and care at a population scale. Nevertheless, substantial research is needed to ensure effective and fair implementation of these CDS and PHM interventions. + + + +## Types of questions that can be asked with clinical data + +### Risk Prediction + +Risk prediction in clinical research involves using data to assess the likelihood of certain outcomes or events occurring in patients. This could include predicting the risk of developing a particular disease, experiencing a specific complication, or responding to a treatment. + +Data used for risk prediction can come from various sources, including: + + * Clinical Data: This includes patient demographics, medical history, laboratory results, and imaging studies. + * Genetic Data: Genetic information, such as DNA sequencing results, can provide valuable insights into an individual's susceptibility to certain diseases. + * Environmental and Lifestyle Data: Factors such as diet, exercise habits, smoking status, and environmental exposures can influence disease risk and may be included in risk prediction models. + * Biomarkers: Biological markers indicative of disease or physiological processes can be used as predictors in risk models [@Bodaghi_Fattahi_Ramazani_2023]. + +Once relevant data is collected, statistical and machine learning techniques can be applied to develop predictive models. These models aim to identify patterns and relationships within the data that are associated with the outcome of interest. Common techniques include logistic regression, decision trees, random forests, support vector machines, and neural networks. + +After the model is trained on a dataset, it can be validated using independent datasets to assess its performance and generalizability. Once validated, the model can be used to predict risk in new patients based on their individual characteristics and data. + +Clinical prediction rules are a subset of risk prediction models, specific to clinical research. + +Examples of risk prediction models ... + +Risk prediction models are important because ... + +Overall, risk prediction in clinical research allows healthcare professionals to identify individuals at higher risk of certain outcomes, enabling targeted interventions, personalized treatments, and more efficient resource allocation. + +### Cohort identification for research + +Clinical data plays a crucial role in cohort identification for research purposes. Researchers typically use electronic health records (EHRs), medical databases, or registries to identify cohorts based on specific criteria such as age, gender, medical conditions, treatments, medications, and outcomes. Advanced data mining and natural language processing techniques can also be employed to extract relevant information from unstructured data sources like clinical notes. Once cohorts are identified, researchers can analyze the data to study disease progression, treatment effectiveness, and outcomes. + +Cohort identification is important regardless of research study type, but to provide specific examples: + +* Research Design: Identifying cohorts allows researchers to design studies with appropriate inclusion or exclusion criteria. By selecting specific groups of individuals with similar characteristics or exposures, researchers can investigate hypotheses effectively. +* Clinical Insights: Cohort studies enable researchers to observe the natural history of diseases, track outcomes over time, and assess the effectiveness of interventions or treatments. Understanding how different factors influence disease progression or treatment response can inform clinical decision-making and improve patient care. +* Epidemiological Studies: Cohort identification is crucial for epidemiological research to understand the incidence, prevalence, and risk factors associated with diseases. By following cohorts over time, researchers can identify trends, patterns, and associations that contribute to our understanding of disease causation and prevention. +* Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. +* Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. + + +### Clinical studies and trials + +Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. + +Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. + +::: {.definition} +The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." +::: + +The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. + +::: {.definition} +**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. +::: + +Here, we explore the different categories of questions that can be addressed using clinical trials data, ranging from the evaluation of treatment efficacy and safety to the exploration of predictive factors for disease prognosis and the personalization of medical care. + +1. **Efficacy and Effectiveness Questions** + +One of the primary objectives of clinical trials is to determine the **efficacy of new treatments or interventions**. Clinical data is used to answer fundamental questions such as "Is the new treatment effective?" and "How does its effectiveness compare to existing treatments?" + +Researchers design studies to measure the **magnitude of treatment effects**, comparing outcomes between treatment and control groups (e.g., a new drug versus a placebo or standard care). + +Beyond controlled settings, data from real-world evidence (RWE) studies can further address questions about the effectiveness of treatments when applied in routine clinical practice. For example, "Is Drug A more effective than Drug B in managing hypertension in a real-world patient population?" Such questions help to assess how treatments perform outside the controlled environment of clinical trials where patient adherence comorbidities, and polypharmacy may influence outcomes. While clinical trials are an important source of real-world data (RWD), they are typically conducted under tightly controlled conditions, which may not fully reflect the complexities of real-world studies. This makes RWE particularly valuable for understanding how treatments work when patients have varying levels of adherence or multiple chronic conditions that are often excluded from clinical trails. + +::: {.definition} +**Comorbidities**: refer to the presence of one or more additional medical conditions or diseases that coexist with a primary condition in a patient. These conditions can occur independently or be related to the primary disease. +::: + +2. **Safety and Tolerability Questions** + +Safety is a critical aspect of any clinical study, and clinical data is essential in identifying and characterizing the safety profile of new treatments. Questions like "What are the common adverse effects of the new treatment?" or "What is the incidence of serious adverse events (SAEs) in patients receiving Drug A?" are addressed by carefully monitoring and recording adverse events throughout the study. Comparative safety questions also arise, such as "Does Drug A have fewer adverse effects compared to Drug B?" These questions are crucial in weighing the benefits of a treatment against its risk and in identifying specific populations that may be at higher risk of adverse reactions. + +::: {.definition} +**Serious Adverse Events (SAEs)**: Any medical occurrence during a clinical trial or medical treatment that results in significant negative outcomes, such as death, life-threatening situations, hospitalization (or its prolongation), persistent or significant disability, or congenital anomalies. SAEs also include any event that requires intervention to prevent one of these outcomes. +::: + +::: {.definition} +**Incidence**: Refers to the rate or number of new cases of SAEs that occur within a specific population during a defined time period, typically during a clinical trial or study. Is it usually expressed as a proportion, such as the number of SAEs per 100 or 1,000 participants, and helps measure how frequently these serious events happen among those exposed to a particular treatment or intervention. +::: + +3. **Comparative Effectiveness and Cost-Effectiveness Questions** + +Beyond safety and efficacy, clinical data can be used to evaluate the comparative effectiveness of different treatments and their cost-effectiveness. These questions often guide healthcare policy and clinical guidelines: "Is Drug A more cost-effective than standard care for managing chronic heart failure?" and "What is the cost per quality-adjusted life year (QALY) gained with the new intervention?" Such analyses provide valuable information for stakeholders, including healthcare providers, payers, and policymakers, to make informed decisions on resource allocation. + +::: {.definition} +**Cost per quality-adjusted life year (QALY)** is a metric used in health economics to assess the value of a medical intervention by measuring the cost of gaining one year of life adjusted for its quality. A QALY incorporates both the **quantity** and **quality** of life lived, where one QALY is equivalent to one year of life in perfect health. If a treatment improves both the length and quality of life, it earns more QALYs. The **cost per QALY** is calculated by dividing the cost of the treatment by the number of QALYs gained, helping to determine the cost-effectiveness of healthcare interventions. Lower cost per QALY indicates better value for money in terms of health benefits achieved [-@Drummond_Sculpher_Claxton_Stoddart_Torrance_2015]. +::: + +4. **Mechanistic and Biomarker Questions** + +Clinical studies often explore not just whether a treatment works, but also how it works. Mechanistic questions delve into the underlying biological pathways affected by a treatment. For instance, "Does Drug A work by inhibiting a specific enzyme involved in the disease process?" Biomarker data can play a key role in such analyses, helping to identify molecular markers that predict response to treatment. Questions like "Are there biomarkers that can predict which patients are more likely to benefit from Drug A?" help in developing targeted therapies and personalized treatment approaches. + +5. **Predictive and Prognostic Questions** + +Clinical trials and studies frequently aim to identify factors that predict disease outcomes or treatment responses. For example, "What baseline characteristics predict a better response to Drug A?" or "What are the predictors of mortality in patients with severe heart failure?" Such prognostic and predictive questions are crucial for identifying high-risk patients, guiding treatment decisions, and developing clinical guidelines. Identifying patient subgroups that benefit more or less from a treatment also facilitates personalized medicine approaches, ensuring that the right treatment is delivered to the right patient at the right time. + +With the advent of personalized medicine, clinical data is increasingly being used to answer questions tailored to individual patient profiles. For instance, "What patient characteristics modify the effect of the treatment?" or "Can we predict which patients are most likely to benefit from Drug A using machine learning models?" These questions are at the forefront of precision medicine, where the goal is to customize healthcare to each patient based on their unique genetic, biomarker, and clinical characteristics. + +6. **Quality of Life and Patient-Reported Outcome Questions** + +In modern clinical research, the impact of treatments on patients' quality of life and their subjective experiences has gained prominence. Questions such as "How does Drug A affect patients' quality of life compared to standard care?" or "What are the patient-reported outcomes associated with the new intervention?" are increasingly addressed in clinical trials. Data from quality of life assessments which are standardized and validated questionnaires (for example, the Health Assessment Questionnaire or HAQ), patient-reported outcome measures (PROMs, tools or questionnaires used to assess a patient's health status or health-related quality of life directly from the patient's perspective), and other patient-centric endpoints (Smoking History, Surgical History, etc.) provide valuable insights into how treatments affect patients beyond traditional clinical endpoints. + +7. **Longitudinal and Follow-Up Questions** + +Long-term follow-up studies are essential for understanding the durability of treatment effects and any delayed adverse effects. Questions like "What are the long-term outcomes associated with Drug A?" and "Is there a sustained benefit of Drug A in reducing symptoms after five years?" are vital for assessing the overall value of treatments. Such longitudinal analyses help in determining the optimal duration of therapy, the need for maintenance treatments, and the overall benefit-risk profile of an intervention. + +8. **Safety in Special Populations Questions** + +Clinical data can also be used to evaluate the safety and efficacy of treatments in special populations, such as children, pregnant women, or elderly patients. Questions such as "Is Drug A safe for use in pregnant women?" and "What is the risk of adverse events when Drug A is administered to patients with multiple comorbidities?" are essential for developing age- and condition-specific clinical guidelines. These questions help ensure that treatments are tailored to the needs of different patient populations, minimizing harm and maximizing benefits. + +9. **Adherence and Compliance Questions** + +Patient adherence to prescribed treatment regimens can significantly impact the outcomes of clinical studies. Questions like "What factors influence patient adherence to the new treatment?" and "How does adherence affect treatment efficacy?" are addressed through analyzing adherence data through monitors if a regulated study, or with interim/baseline analysis if non-regulated. Understanding these factors can lead to interventions that improve adherence and, consequently, the effectiveness of the treatment. + +10. **Real-World Evidence and Generalizability Questions** + +Real-world evidence (RWE) studies help bridge the gap between clinical trial results and clinical practice. Questions such as "How generalizable are the results of this clinical trial to the broader population?" or "What is the impact of real-world use patterns on treatment outcomes?" are essential for understanding how well clinical trial findings apply to everyday clinical settings. These questions help determine if the benefits observed in clinical trials can be replicated in diverse patient populations under routine care conditions. + +11. **Prevention and Risk Reduction Questions** + +Clinical data is also crucial in answering questions related to prevention and risk reduction strategies. For example, "Can the new treatment reduce the risk of developing diabetes in high-risk individuals?" or "What factors are associated with a reduced risk of cardiovascular disease in a large cohort study?" These questions are fundamental in preventive medicine, guiding public health interventions and informing clinical practice. + + +Clinical Data provides a wealth of information that can be leveraged to answer a broad array of questions in clinical trials and studies. From understanding treatment efficacy and safety to exploring long-term outcomes, quality of life impacts, and the potential for personalized medicine, clinical data is foundational to advancing medical knowledge and improving patient care. Case report forms (CRFs) are instrumental in identifying patient cohorts or subgroups, documenting baseline characteristics, and capturing information on comorbidities, while also monitoring adherence data, which are essential for ensuring accurate and meaningful analysis. The type of questions that can be asked and answered are continually evolving as new data sources, analytical methods, and research paradigms emerge, further enriching the field of clinical research. + + +### Retrospective analysis + +Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. + +Here, we explore the different categories of questions that can be addressed using retrospective data analysis: + +1. **Descriptive Questions** + +Descriptive questions aim to summarize and describe the characteristics of a patient population, disease, or healthcare process. They provide a foundational understanding of a dataset and are often the starting point for more complex analyses. + +- **Patient Demographics and Characteristics**: What are the demographic profiles (age, gender, ethnicity, socioeconomic status) of patients diagnosed with a specific condition? What are the common comorbidities and risk factors in this population? +- **Disease Prevalence and Incidence**: What is the prevalence or incidence of a specific disease or condition in a particular population or geographic area over a defined period? +- **Clinical Presentation**: What are the most common presenting symptoms, clinical signs, or laboratory findings associated with a specific disease or condition? + +These questions help in understanding the baseline characteristics and epidemiology of diseases, which is crucial for planning further studies or developing public health interventions. + +2. **Treatment and Intervention Questions** + +Retrospective data can be used to examine the real-world effectiveness, safety, and patterns of treatment and interventions. These questions are critical for understanding how treatments are applied in clinical practice and their outcomes: + +- **Treatment Effectiveness**: What is the effectiveness of a specific treatment or intervention in reducing symptoms, improving quality of life, or achieving clinical outcomes in a real-world setting? +- **Adherence and Persistence**: What are the rates of adherence and persistence with a prescribed treatment regimen over time? What factors are associated with higher or lower adherence rates? +- **Comparative Effectiveness**: How does the effectiveness of one treatment compare to another in a similar population? What are the relative benefits and risks associated with different treatment options? + +By examining historical data, researchers can gain insights into how different treatments perform outside the controlled environment of clinical trials, thereby informing clinical decision-making and guidelines. + +3. **Outcome and Prognostic Questions** + +Understanding patient outcomes and prognostic factors is central to retrospective analyses. These questions focus on the end results of healthcare practices and patient management, including survival, complications, and quality of life: + +- **Survival and Mortality**: What are the survival rates and mortality rates associated with a particular disease, condition, or treatment? What factors are associated with increased or decreased survival rates? +- **Complication Rates**: What are the rates and types of complications associated with specific diseases, surgeries, or treatments? Are there identifiable risk factors for these complications? +- **Prognostic Factors**: What are the key prognostic factors (e.g., age, stage of disease, comorbidities) that influence the outcomes of patients with a particular condition? + +These questions help identify factors that influence patient outcomes, guiding clinicians in predicting disease progression and tailoring individual treatment plans. + +4. Risk Factor and Predictive Modeling Questions + +Retrospective analyses are often used to identify potential risk factors for diseases and develop predictive models. These questions aim to uncover associations between variables and outcomes to help predict future events: + +- **Risk Factors for Disease**: What are the risk factors associated with developing a particular disease or condition? Are there demographic, genetic, behavioral, or environmental factors that significantly increase the risk? +- **Prediction Models**: Can we develop a predictive model to estimate the likelihood of disease progression, relapse, or adverse outcomes based on historical patient data? +- **Multivariable Analysis**: How do multiple factors interact to influence the risk of an outcome? Are there synergistic or antagonistic effects between different risk factors? + +These questions are critical for developing tools that help clinicians assess risk and make more informed decisions in preventive care and early intervention. + +5. **Healthcare Utilization and Cost Questions** + +Understanding how healthcare resources are utilized and the associated costs is important for optimizing healthcare delivery. Retrospective data can provide valuable insights into patterns of care and resource allocation for questions such as: + +- **Healthcare Resource Utilization**: What are the patterns of healthcare utilization (e.g., hospitalizations, emergency room visits, outpatient visits) for patients with a specific condition? How does utilization vary by patient demographics, disease severity, or geographic location? +- **Cost of Care**: What are the direct and indirect costs associated with managing a specific disease or condition? How do different treatment options impact overall healthcare costs? +- **Cost-Effectiveness**: How does the cost-effectiveness of one treatment or intervention compare to another? What are the economic impacts of different clinical management strategies? + +These questions can inform healthcare policy, budget planning, and the development of strategies to improve cost-effectiveness and access to care. + +6. **Quality of Care and Patient Safety Questions** + +Retrospective analysis can be used to evaluate the quality and safety of care provided to patients, identifying areas for improvement and potential patient safety concerns, helping to answer questions such as: + +- **Quality Indicators**: Quality indicators are standardized measures of health care quality and may focus on more specialized groups of care like preventative, inpatient or emergency care, pediatric or maternal health, etc. What are the levels of observed mortality or morbidity for a certain population in a specific setting? How utilized are certain procedures? [@AHRQ] +- **Adverse Events and Safety**: What is the incidence of adverse events, medication errors, or healthcare-associated infections in a particular patient population? What factors are associated with higher or lower rates of adverse events? +- **Patient Satisfaction and Experience**: What are the reported levels of patient satisfaction with healthcare services? How does satisfaction vary by demographic or clinical characteristics? + +These questions help in assessing healthcare quality, identifying gaps in care, and developing interventions to enhance patient safety and satisfaction. + +7. **Temporal and Trend Analysis Questions** + +Retrospective studies often involve analyzing temporal patterns to understand how diseases, treatments, or outcomes change over time. These questions help in recognizing trends and patterns that can inform future research and policy decisions: + +- **Temporal Trends in Disease Incidence and Prevalence**: How have the incidence and prevalence of a specific disease changed over the past decade? Are there seasonal or cyclical patterns in disease occurrence? +- **Trends in Treatment Practices**: How have treatment practices and prescribing patterns evolved over time? Are there emerging trends in the use of certain medications or interventions? +- **Trends in Outcomes and Survival**: How have patient outcomes, such as survival rates or complication rates, changed over time with advances in treatment and healthcare delivery? + +These questions are essential for monitoring public health trends, evaluating the impact of interventions, and planning future healthcare strategies. + +8. **Subgroup and Stratified Analysis Questions** + +Subgroup analyses can help uncover differences in disease characteristics, treatment responses, and outcomes among different patient groups. This type of analysis is particularly valuable for personalized medicine and addressing questions related to health disparities: + +- **Differences by Demographic Group**: How do treatment outcomes, risk factors, or disease characteristics differ between various demographic groups (e.g., age, gender, race/ethnicity)? +- **Subgroup-Specific Effectiveness**: Does a particular treatment work better in one subgroup of patients compared to another? What factors might explain these differences? +- **Health Disparities**: Are there disparities in healthcare access, treatment outcomes, or disease burden across different socioeconomic or minority groups? + +Questions focusing on subgroups are crucial for understanding variations in care and outcomes, identifying at-risk populations, and developing targeted interventions. + + +Retrospective analysis of clinical data allows researchers to ask a wide range of questions that provide valuable insights into patient characteristics, disease epidemiology, treatment effectiveness, outcomes, risk factors, healthcare utilization, and more. By carefully formulating questions and analyzing historical data, researchers can uncover patterns, identify trends, and generate evidence that informs clinical practice, healthcare policy, and future research directions. This chapter outlines the various categories of questions that can be addressed through retrospective analysis, highlighting the potential of this approach to advance knowledge and improve patient care in diverse clinical settings. + + +## Research Design Considerations + +Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. + +One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, + +* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. +* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. +* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. +* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. + +Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: + +* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. +* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. +* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. +* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. + +With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. + +## Conclusion diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/E2086159-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/E2086159-contents new file mode 100644 index 0000000..dc638be --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/E2086159-contents @@ -0,0 +1,10 @@ +# ignore nonsense files +.DS_Store +.Rhistory +.local +.rstudio +.bash_history +spell_check_results.tsv +.RData +.httr-oauth +docker/git_token.txt diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/F5F672FD-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/F5F672FD-contents new file mode 100644 index 0000000..87b4a01 --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/F5F672FD-contents @@ -0,0 +1,17 @@ +bookdown::gitbook: + css: assets/style_ITN.css + includes: + before_body: assets/big-image-itcr.html + after_body: assets/footer.html + highlight: tango + config: + toc: + collapse: section + before: | + + after: | +

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diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/FB2EFA6D-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/FB2EFA6D-contents new file mode 100644 index 0000000..be8d1be --- /dev/null +++ b/.Rproj.user/6F8C099B/sources/session-87f78f51/FB2EFA6D-contents @@ -0,0 +1,23 @@ +--- +title: "Informatics Technology for Cancer Research (ITCR): Clinical Data Management " +date: "`r format(Sys.time(), '%B, %Y')`" +site: bookdown::bookdown_site +documentclass: book +bibliography: [book.bib, packages.bib] +biblio-style: apalike +link-citations: yes +description: "Description about Course/Book." +favicon: assets/ITN_favicon.ico +--- + + +```{r include=FALSE} +# automatically create a bib database for R packages +knitr::write_bib(c( + .packages(), "bookdown", "knitr", "rmarkdown" +), "packages.bib") +``` + +# About this Course {-} + +This course is part of a series of courses created for the [Informatics Technology for Cancer Research (ITCR)](https://www.cancer.gov/about-nci/organization/cssi/research/itcr) Training Network (ITN). The ITN is a collaborative effort of researchers around the United States that supports cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: [National Cancer Institute (NCI)](https://www.cancer.gov/) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our [website](https://www.itcrtraining.org) at itcrtraining.org for more information. Except where otherwise indicated, the contents of this course are available for use under the Creative Commons Attribution 4.0 license. You are free to adapt and share the work, but you must give appropriate credit, provide a link to the license, and indicate if changes were made. Sample attribution: Cancer Data Management by Fred Hutchinson Data Science Lab (CC-BY 4.0). You can download the illustrations by clicking on this [link for our slides](https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?usp=sharing). diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/lock_file b/.Rproj.user/6F8C099B/sources/session-87f78f51/lock_file new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/6F8C099B87f78f51/chunks.json b/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/6F8C099B87f78f51/chunks.json new file mode 100644 index 0000000..92e21b0 --- /dev/null +++ b/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/6F8C099B87f78f51/chunks.json @@ -0,0 +1 @@ +{"chunk_definitions":[],"doc_write_time":1781803473} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/s/chunks.json b/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/s/chunks.json new file mode 100644 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@@ -0,0 +1 @@ +{"chunk_definitions":[],"doc_write_time":1781029074} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/patch-chunk-names b/.Rproj.user/shared/notebooks/patch-chunk-names new file mode 100644 index 0000000..e69de29 diff --git a/.Rproj.user/shared/notebooks/paths b/.Rproj.user/shared/notebooks/paths new file mode 100644 index 0000000..0283deb --- /dev/null +++ b/.Rproj.user/shared/notebooks/paths @@ -0,0 +1,12 @@ +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/.gitignore="A6915589" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/00-intro.Rmd="7C67BFC2" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/01-data_types.Rmd="1605F509" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/01b-specific_data_types.Rmd="C66DD1BA" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/01c-how_to_collect_data.Rmd="0B1EC249" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/01d-how_to_acquire_data.Rmd="340858D9" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/02-data_uses.Rmd="684837E8" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/03-data_management.Rmd="83E71A98" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/_output.yml="43C147FE" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/index-itcr.Rmd="7AAF009B" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/index.Rmd="341213ED" +/Users/steichma/Documents/clinical_data_course/Clinical_Data_Management/style-sets/itcr/copy-to-assets/style_ITN.css="A9EC4617" diff --git a/01c-how_to_collect_data.Rmd b/01c-how_to_collect_data.Rmd index 4e3ed38..ea0f654 100644 --- a/01c-how_to_collect_data.Rmd +++ b/01c-how_to_collect_data.Rmd @@ -4,12 +4,141 @@ ## Case Report Forms (CRFs) +In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter -- specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). + +Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on **accessibility, sensitivity, specificity, and comfort for people** + +::: {.notice} + +While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren't necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions -- the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. + +::: + +Here, we explore the different categories of questions that may be included within case report forms: + +1. **Demographic and Socioeconomic Questions** + +Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. + +- **Sex and Gender**: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. +- **Ethnicity and Race**: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. +- **Socioeconomic Status**: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, "What is your current employment status?" with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. + +It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. + +2. **Health and Medial History Questions** + +Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. + +- **Medical Conditions and History**: Questions about past and present health conditions should use clear, accessible language. For example, "Have you ever been diagnosed with any of the following conditions? (Please check all that apply)" followed by a comprehensive list containing all necessary options. +- **Medication Use**: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. +- **Disability and Functional Status**: For many populations, it is important to use person-first language, such as "Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?" and provide space for participants to describe their specific needs. However, different populations have different preferences. The "Disability Language Style Guide" from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic [@ncdj_style_guide]. + +Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. + +3. **Experience and Quality of Life Questions** + +Understanding how health conditions and treatments affect participants' daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. + +- **Daily Living and Social Functioning**: Questions like "How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?" can help assess the impact on daily life, with options ranging from "never" to "always." +- **Emotional and Psychological Well-Being**: Including questions such as "In the past week, how often have you felt anxious or depressed?" using a scale from "not at all" to "very often" can provide insights into mental health needs. +- **Support Systems and Social Networks**: Asking about social support (e.g., "Do you have someone you can rely on for emotional support?") can help identify participants' needs for social and emotional resources. + +Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. + +4. **Sexual and Reproductive Health Questions** + +Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. + +- **Sexual Orientation and Gender Identity (SOGI)**: Instead of just predefined categories, open-ended questions like "How would you describe your sexual orientation?" and "What is your gender identity?" allow participants to self-identify. Offering the option to skip these questions respects participants' privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. +- **Reproductive Health**: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, "Are you currently using any form of contraception? If yes, please specify." +- **Sexual Activity and History**: Questions should be direct but framed sensitively, such as "Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs." + +These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. + +5. **Treatment Preferences and Decision-Making Questions** + +Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. + +- **Decision-Making Preferences**: Questions like "How involved would you like to be in decisions about your healthcare?" offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. +- **Cultural and Religious Considerations**: Asking, "Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?" ensures that care is respectful and culturally appropriate. +- **Treatment Burden**: Questions such as "What level of inconvenience or side effects would be acceptable to you when considering a treatment?" help to gauge participants' preferences and comfort levels. + +These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. + +6. **Accessibility and Accommodation Needs Questions** + +To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. + +- **Language and Communication Needs**: "What is your preferred language for communication? Do you need an interpreter or translated materials?" These questions help ensure that participants can understand the materials. +- **Physical Accessibility**: Asking, "Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?" ensures physical accessibility. +- **Format Preferences**: "Would you prefer to complete this form online, on paper, or verbally with assistance?" helps accommodate different needs and preferences. +Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. + +Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. + +7. **Cultural Sensitivity and Identity Questions** + +CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. + +- **Cultural Identity and Practices**: An open-ended question such as "Are there any cultural practices or beliefs that are important for us to be aware of in your care?" allows participants to share relevant information. +- **Dietary Restrictions and Preferences**: Asking, "Do you have any dietary restrictions or preferences that are culturally or religiously motivated?" ensures that these are respected. +- **Community and Belonging**: "Is there anything about your community or background that you would like us to know to provide better care?" encourages participants to share relevant aspects of their identity. + +These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. + +8. **Ensuring Comfort, Trust, and Privacy Questions** + +Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. + +- **Comfort and Confidentiality**: Asking, "Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?" helps build trust. +- **Feedback and Preferences**: "Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?" invites participants to share their feedback. +- **Consent and Voluntary Participation**: Questions like, "Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary." reinforce autonomy and respect. + +Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. + +::: {.warning} + +The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. + +::: + +Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. By thoughtfully addressing both unique study requirements and universally relevant data points, researchers can optimize CRFs for consistency across clinical studies. + ## Clinical Studies and Trials -## Retrospective Analyses +Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. + +Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. + +::: {.definition} +The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." +::: + +The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. -Does this belong in collecting data or acquiring data? +::: {.definition} +**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. +::: ## Research Design Considerations +Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. + +One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, + +* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. +* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. +* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. +* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. + +Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: + +* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. +* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. +* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. +* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. + +With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. + ## Summary diff --git a/01d-how_to_acquire_data.Rmd b/01d-how_to_acquire_data.Rmd index e5d0cb6..74fe804 100644 --- a/01d-how_to_acquire_data.Rmd +++ b/01d-how_to_acquire_data.Rmd @@ -10,4 +10,10 @@ ## Secondary Sources +## Retrospective Analyses + +Does this belong in collecting data or acquiring data? + +Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. + ## Summary diff --git a/_bookdown_files/02-chapter_of_course_files/figure-html/unnamed-chunk-2-1.png b/02-chapter_of_course_files/figure-html/unnamed-chunk-2-1.png similarity index 100% rename from _bookdown_files/02-chapter_of_course_files/figure-html/unnamed-chunk-2-1.png rename to 02-chapter_of_course_files/figure-html/unnamed-chunk-2-1.png diff --git a/_bookdown_files/02-chapter_of_course_files/figure-html/unnamed-chunk-3-1.png b/02-chapter_of_course_files/figure-html/unnamed-chunk-3-1.png similarity index 100% rename from _bookdown_files/02-chapter_of_course_files/figure-html/unnamed-chunk-3-1.png rename to 02-chapter_of_course_files/figure-html/unnamed-chunk-3-1.png diff --git a/02-data_uses.Rmd b/02-data_uses.Rmd index d5748c3..d7dd267 100644 --- a/02-data_uses.Rmd +++ b/02-data_uses.Rmd @@ -96,109 +96,6 @@ Cohort identification is important regardless of research study type, but to pro * Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. * Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. -### Case report forms (CRFs) - -In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter -- specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). - -Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on **accessibility, sensitivity, specificity, and comfort for people** - -::: {.notice} - -While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren't necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions -- the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. - -::: - -Here, we explore the different categories of questions that may be included within case report forms: - -1. **Demographic and Socioeconomic Questions** - -Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. - -- **Sex and Gender**: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. -- **Ethnicity and Race**: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. -- **Socioeconomic Status**: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, "What is your current employment status?" with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. - -It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. - -2. **Health and Medial History Questions** - -Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. - -- **Medical Conditions and History**: Questions about past and present health conditions should use clear, accessible language. For example, "Have you ever been diagnosed with any of the following conditions? (Please check all that apply)" followed by a comprehensive list containing all necessary options. -- **Medication Use**: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. -- **Disability and Functional Status**: For many populations, it is important to use person-first language, such as "Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?" and provide space for participants to describe their specific needs. However, different populations have different preferences. The "Disability Language Style Guide" from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic [@ncdj_style_guide]. - -Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. - -3. **Experience and Quality of Life Questions** - -Understanding how health conditions and treatments affect participants' daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. - -- **Daily Living and Social Functioning**: Questions like "How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?" can help assess the impact on daily life, with options ranging from "never" to "always." -- **Emotional and Psychological Well-Being**: Including questions such as "In the past week, how often have you felt anxious or depressed?" using a scale from "not at all" to "very often" can provide insights into mental health needs. -- **Support Systems and Social Networks**: Asking about social support (e.g., "Do you have someone you can rely on for emotional support?") can help identify participants' needs for social and emotional resources. - -Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. - -4. **Sexual and Reproductive Health Questions** - -Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. - -- **Sexual Orientation and Gender Identity (SOGI)**: Instead of just predefined categories, open-ended questions like "How would you describe your sexual orientation?" and "What is your gender identity?" allow participants to self-identify. Offering the option to skip these questions respects participants' privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. -- **Reproductive Health**: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, "Are you currently using any form of contraception? If yes, please specify." -- **Sexual Activity and History**: Questions should be direct but framed sensitively, such as "Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs." - -These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. - -5. **Treatment Preferences and Decision-Making Questions** - -Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. - -- **Decision-Making Preferences**: Questions like "How involved would you like to be in decisions about your healthcare?" offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. -- **Cultural and Religious Considerations**: Asking, "Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?" ensures that care is respectful and culturally appropriate. -- **Treatment Burden**: Questions such as "What level of inconvenience or side effects would be acceptable to you when considering a treatment?" help to gauge participants' preferences and comfort levels. - -These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. - -6. **Accessibility and Accommodation Needs Questions** - -To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. - -- **Language and Communication Needs**: "What is your preferred language for communication? Do you need an interpreter or translated materials?" These questions help ensure that participants can understand the materials. -- **Physical Accessibility**: Asking, "Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?" ensures physical accessibility. -- **Format Preferences**: "Would you prefer to complete this form online, on paper, or verbally with assistance?" helps accommodate different needs and preferences. -Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. - -Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. - -7. **Cultural Sensitivity and Identity Questions** - -CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. - -- **Cultural Identity and Practices**: An open-ended question such as "Are there any cultural practices or beliefs that are important for us to be aware of in your care?" allows participants to share relevant information. -- **Dietary Restrictions and Preferences**: Asking, "Do you have any dietary restrictions or preferences that are culturally or religiously motivated?" ensures that these are respected. -- **Community and Belonging**: "Is there anything about your community or background that you would like us to know to provide better care?" encourages participants to share relevant aspects of their identity. - -These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. - -8. **Ensuring Comfort, Trust, and Privacy Questions** - -Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. - -- **Comfort and Confidentiality**: Asking, "Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?" helps build trust. -- **Feedback and Preferences**: "Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?" invites participants to share their feedback. -- **Consent and Voluntary Participation**: Questions like, "Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary." reinforce autonomy and respect. - -Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. - -::: {.warning} - -The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. - -::: - -Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. 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diff --git a/docs/introduction.html b/docs/introduction.html index 0cde38f..44dc48d 100644 --- a/docs/introduction.html +++ b/docs/introduction.html @@ -4,18 +4,18 @@ - Chapter 1 Introduction | Clinical Data Analysis for Cancer Informatics + Chapter 1 Introduction | Appendix I - + - + @@ -57,28 +57,6 @@ div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} - @@ -190,7 +168,7 @@
diff --git a/docs/search_index.json b/docs/search_index.json index 20ed5e0..cbbda58 100644 --- a/docs/search_index.json +++ b/docs/search_index.json @@ -1 +1 @@ -[["index.html", "Clinical Data Analysis for Cancer Informatics About this Course", " Clinical Data Analysis for Cancer Informatics May, 2026 About this Course This course is part of a series of courses created for the Informatics Technology for Cancer Research (ITCR) Training Network (ITN). The ITN is a collaborative effort of researchers around the United States that supports cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: National Cancer Institute (NCI) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our website at itcrtraining.org for more information. Except where otherwise indicated, the contents of this course are available for use under the Creative Commons Attribution 4.0 license. You are free to adapt and share the work, but you must give appropriate credit, provide a link to the license, and indicate if changes were made. Sample attribution: Cancer Data Management by Fred Hutchinson Data Science Lab (CC-BY 4.0). You can download the illustrations by clicking on this link for our slides. "],["introduction.html", "Chapter 1 Introduction 1.1 Motivation 1.2 Topics Covered 1.3 Curriculum", " Chapter 1 Introduction 1.1 Motivation This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines. This course is intended to provide an overview of the different kinds of data commonly used in clinical research, the questions that can typically be asked with such data, as well as guidance for how to manage this data. 1.2 Topics Covered 1.3 Curriculum The course will cover key underlying principles and concepts in ethical data handling. "],["clinical-data-types.html", "Chapter 2 Clinical Data Types 2.1 Learning Objectives 2.2 Clinical data is unique 2.3 Major clinical data types 2.4 How to acquire clinical data 2.5 Description of data 2.6 Summary", " Chapter 2 Clinical Data Types Clinical data is health-related information collected from patients throughout their healthcare journey. It may come in many forms and its sensitive nature requires careful management by researchers. 2.1 Learning Objectives 2.2 Clinical data is unique Clinical data refers to information collected from patients during healthcare delivery, clinical trials, and medical research. Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data with predictable and consistent formats, clinical data is often heterogeneous, integrating many forms of both structured and unstructured data: quantitative measurements, categorical data, genotyping, images, subjective narratives from patient notes, and objective observations or conclusions. The unstructured nature of free text from notes reflecting subjective patient experiences or qualitative insights from healthcare professionals especially adds a further layer of complexity. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data’s unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare. Further, because clinical data contains sensitive, personal information about patients, there are additional security and ethics concerns in the handling and management of clinical data. 2.3 Major clinical data types Clinical data can come in many different forms, including patient demographics medical history or records such as diagnoses, lab results, vital signs, medication records, or procedure history genetic reports health monitor data images scanned documents, and notes written by physicians, nurses, and other clinicians survey/ case report form (CRF responses) and more … Some sources of clinical data are more prevalent and readily obtainable than others. For instance, notes, demographics, images, and histories or observations/records (lab results, vitals, medications, procedures) are often stored directly in electronic medical record systems making them more easily accessible and so are the focus of most Electronic Health Record (EHR) data research efforts. 2.3.1 Structured data Observational records such as test results and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Structured data types frequently used in EHR research consist of comprehensive longitudinal records of a patient’s interactions with a healthcare system and may include demographics, diagnoses, lab values, procedures, vitals, and medication records. The structured nature of this data allows for it to be stored in tables which may be indexed by a patient or visit ID and often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or “billing codes”, provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. As we’ve described it, structured clinical data is expected to have similarities, although specific details may vary based on the type of EHR being used and any customizations to the EHR for the specific environment or institution in which data was collected (e.g., any specialized pre-processing by institutional research offices prior to providing data to researchers). So it is important for researchers to consider differences present in their data if they’ve obtained it from multiple contexts or institutions. 2.3.2 Unstructured data / clinical notes Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” (Dhir et al. 2008) charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. 2.4 How to acquire clinical data 2.4.1 Secondary Sources 2.5 Description of data 2.5.1 File types 2.5.2 Metadata 2.6 Summary References "],["specific-clinical-data-types.html", "Chapter 3 Specific Clinical Data Types 3.1 Learning Objectives 3.2 Physiological 3.3 Monitoring data 3.4 Radiology 3.5 Pathology 3.6 Synthetic Data 3.7 Summary", " Chapter 3 Specific Clinical Data Types 3.1 Learning Objectives 3.2 Physiological 3.3 Monitoring data This section was written by: Jennifer Kelleher, Ph.D.1; Abigail S. Robbertz, Ph.D.1; and Meghan E. McGrady, Ph.D.1,2 NOTE: Jennifer Kelleher, Ph.D.1 and Abigail S. Robbertz, Ph.D.1 contributed equally. 1 Center for Adherence and Self-Management, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA 2 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA The work discussed in this section was also supported by the National Cancer Institute at the National Institutes of Health (R21CA263704, K07CA200668) to MEM. JK and ASR are supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development at the National Institutes of Health (T32HD068223). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Electronic monitoring devices are digital tools that can be used to track health behaviors over time such as: Sleep Physical activity Medication adherence Calorie intake Electronic monitoring devices can also be used to assess physical health indicators including: * Blood glucose levels * Blood pressure * Heart rate and heart rate variability * Oxygen saturation Electronic monitoring devices enable researchers to track day-to-day health behaviors in the patient’s “real-world” setting. This allows researchers to explore patterns or changes in a patient’s health behavior and provides a richer understanding of daily behavior over time. 3.3.1 Benefits of Monitoring Data Electronic monitoring devices often include data transmission abilities that enable healthcare providers or researchers to access these data in near real-time potentially informing intervention and/or medical decision-making. Electronic monitoring devices also have the potential to produce more accurate estimates of health behaviors than alternative strategies (e.g., self-report) as they are not subject to recall bias and can detect efforts to inflate adherence due to social desirability. 3.3.2 Considerations This section is not exhaustive. Research teams are strongly encouraged to consult with experts with experience and training in collecting and analyzing data from specific devices. To ensure the outcome variables are aligned with the research question of interest and ethical and age/developmental considerations (Psihogios et al. (2024); Modi et al. (2012)) have been appropriately accounted for, readers are encouraged to consult with researchers in their field who have integrated these measurement strategies into their work. 3.3.2.1 Medication Adherence There are three major components of medical adherence (the tracking of taking medication): Initiation: Starting a prescribed regimen Implementation: The amount of which a patient’s medication-taking behavior corresponds with the treatment regimen or protocol Discontinuation: Stopping a perscribed regimen For more information see: A new taxonomy for describing and defining adherence to medications (Vrijens et al., 2012) Pediatric self-management: A framework for research, practice, and policy. (Modi et al., 2012) 3.4 Radiology 3.5 Pathology 3.6 Synthetic Data 3.7 Summary References "],["clinical-data-uses.html", "Chapter 4 Clinical Data Uses 4.1 Learning Objectives 4.2 General uses of clinical data 4.3 Types of questions that can be asked with clinical data 4.4 Research Design Considerations 4.5 Conclusion", " Chapter 4 Clinical Data Uses 4.1 Learning Objectives 4.2 General uses of clinical data 4.2.1 Cancer Prevention and Care The near universal adoption of electronic health record (EHR) systems in the US has created unprecedented opportunities to improve cancer prevention and care. As described in previous chapters, EHR systems store comprehensive longitudinal records of a patient’s interactions with a healthcare system, including data about: demographics conditions family history clinical notes laboratory test results medications procedures imaging genetic test reports These data can be accessed not only by health professionals, but also by patients through patient portals. EHR data can also be used to enable data-driven interventions such as provider- and patient-facing clinical decision support (CDS) and population health management (PHM). CDS has been defined as tools that “provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care” (Osheroff et al. 2007). Examples of widely adopted CDS tools with demonstrated effectiveness for cancer prevention, diagnosis, and care include: provider and patient reminders for cancer screening decision aids and shared decision-making tools (e.g., for prostate cancer treatment, lung cancer screening) AI-supported diagnostic imaging chemotherapy decision support at-home symptom care While CDS tools generally provide decision support focused on a specific patient at a time, PHM are strategies that target specific patient populations (Swarthout and Bishop 2017). PHM efforts generally consist of: population algorithms that are applied over EHR and other data sources to identify individuals who are eligible for a specific healthcare intervention (e.g., colorectal cancer screening, tobacco cessation, HPV vaccination) patient engagement strategies (e.g., patient portals, text messaging, chatbots, patient navigators) that provide education and access to those interventions analytic tools that assess the effectiveness of the PHM program. Several PHM programs have demonstrated to be effective in increasing the uptake of cancer prevention. For example, the colorectal cancer screening program at Kaiser Permanente uses digital (i.e., text messaging, patient portal), mailed, and patient navigation approaches to increase colorectal cancer screening by mailing Fecal Immunohistochemical Test (FIT) kits to patients’ homes (Gupta et al. 2020). Also the Cancer Moonshot BRIDGE trial used the GARDE platform (Bradshaw et al. 2022) (ITCR-funded) to identify candidates for genetic testing of hereditary cancer syndromes based on EHR data; and for patient outreach, pre- and post-test education via automated chatbots (Kaphingst et al. 2024). Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. While some CDS and PHM approaches have been successfully adopted widely, emerging technologies such as the use of generative AI approaches to analyze diagnostic imaging, large language models (LLMs) to extract information from narrative texts (e.g., clinical notes), LLM-based chatbots to communicate with patients, and digital health tools such as home-based sensors are creating unprecedented opportunities for next generation CDS and PHM. These approaches have the potential to enable significant breakthroughs through the implementation of patient-tailored cancer prevention and care at a population scale. Nevertheless, substantial research is needed to ensure effective and fair implementation of these CDS and PHM interventions. 4.3 Types of questions that can be asked with clinical data 4.3.1 Risk Prediction Risk prediction in clinical research involves using data to assess the likelihood of certain outcomes or events occurring in patients. This could include predicting the risk of developing a particular disease, experiencing a specific complication, or responding to a treatment. Data used for risk prediction can come from various sources, including: Clinical Data: This includes patient demographics, medical history, laboratory results, and imaging studies. Genetic Data: Genetic information, such as DNA sequencing results, can provide valuable insights into an individual’s susceptibility to certain diseases. Environmental and Lifestyle Data: Factors such as diet, exercise habits, smoking status, and environmental exposures can influence disease risk and may be included in risk prediction models. Biomarkers: Biological markers indicative of disease or physiological processes can be used as predictors in risk models (Bodaghi, Fattahi, and Ramazani 2023). Once relevant data is collected, statistical and machine learning techniques can be applied to develop predictive models. These models aim to identify patterns and relationships within the data that are associated with the outcome of interest. Common techniques include logistic regression, decision trees, random forests, support vector machines, and neural networks. After the model is trained on a dataset, it can be validated using independent datasets to assess its performance and generalizability. Once validated, the model can be used to predict risk in new patients based on their individual characteristics and data. Clinical prediction rules are a subset of risk prediction models, specific to clinical research. Examples of risk prediction models … Risk prediction models are important because … Overall, risk prediction in clinical research allows healthcare professionals to identify individuals at higher risk of certain outcomes, enabling targeted interventions, personalized treatments, and more efficient resource allocation. 4.3.2 Cohort identification for research Clinical data plays a crucial role in cohort identification for research purposes. Researchers typically use electronic health records (EHRs), medical databases, or registries to identify cohorts based on specific criteria such as age, gender, medical conditions, treatments, medications, and outcomes. Advanced data mining and natural language processing techniques can also be employed to extract relevant information from unstructured data sources like clinical notes. Once cohorts are identified, researchers can analyze the data to study disease progression, treatment effectiveness, and outcomes. Cohort identification is important regardless of research study type, but to provide specific examples: Research Design: Identifying cohorts allows researchers to design studies with appropriate inclusion or exclusion criteria. By selecting specific groups of individuals with similar characteristics or exposures, researchers can investigate hypotheses effectively. Clinical Insights: Cohort studies enable researchers to observe the natural history of diseases, track outcomes over time, and assess the effectiveness of interventions or treatments. Understanding how different factors influence disease progression or treatment response can inform clinical decision-making and improve patient care. Epidemiological Studies: Cohort identification is crucial for epidemiological research to understand the incidence, prevalence, and risk factors associated with diseases. By following cohorts over time, researchers can identify trends, patterns, and associations that contribute to our understanding of disease causation and prevention. Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. 4.3.3 Case report forms (CRFs) In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter – specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on accessibility, sensitivity, specificity, and comfort for people While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren’t necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions – the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. Here, we explore the different categories of questions that may be included within case report forms: Demographic and Socioeconomic Questions Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. Sex and Gender: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. Ethnicity and Race: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. Socioeconomic Status: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, “What is your current employment status?” with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. Health and Medial History Questions Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. Medical Conditions and History: Questions about past and present health conditions should use clear, accessible language. For example, “Have you ever been diagnosed with any of the following conditions? (Please check all that apply)” followed by a comprehensive list containing all necessary options. Medication Use: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. Disability and Functional Status: For many populations, it is important to use person-first language, such as “Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?” and provide space for participants to describe their specific needs. However, different populations have different preferences. The “Disability Language Style Guide” from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic (“Disability Language Style Guide” 2021). Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. Experience and Quality of Life Questions Understanding how health conditions and treatments affect participants’ daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. Daily Living and Social Functioning: Questions like “How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?” can help assess the impact on daily life, with options ranging from “never” to “always.” Emotional and Psychological Well-Being: Including questions such as “In the past week, how often have you felt anxious or depressed?” using a scale from “not at all” to “very often” can provide insights into mental health needs. Support Systems and Social Networks: Asking about social support (e.g., “Do you have someone you can rely on for emotional support?”) can help identify participants’ needs for social and emotional resources. Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. Sexual and Reproductive Health Questions Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. Sexual Orientation and Gender Identity (SOGI): Instead of just predefined categories, open-ended questions like “How would you describe your sexual orientation?” and “What is your gender identity?” allow participants to self-identify. Offering the option to skip these questions respects participants’ privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. Reproductive Health: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, “Are you currently using any form of contraception? If yes, please specify.” Sexual Activity and History: Questions should be direct but framed sensitively, such as “Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs.” These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. Treatment Preferences and Decision-Making Questions Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. Decision-Making Preferences: Questions like “How involved would you like to be in decisions about your healthcare?” offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. Cultural and Religious Considerations: Asking, “Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?” ensures that care is respectful and culturally appropriate. Treatment Burden: Questions such as “What level of inconvenience or side effects would be acceptable to you when considering a treatment?” help to gauge participants’ preferences and comfort levels. These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. Accessibility and Accommodation Needs Questions To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. Language and Communication Needs: “What is your preferred language for communication? Do you need an interpreter or translated materials?” These questions help ensure that participants can understand the materials. Physical Accessibility: Asking, “Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?” ensures physical accessibility. Format Preferences: “Would you prefer to complete this form online, on paper, or verbally with assistance?” helps accommodate different needs and preferences. Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. Cultural Sensitivity and Identity Questions CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. Cultural Identity and Practices: An open-ended question such as “Are there any cultural practices or beliefs that are important for us to be aware of in your care?” allows participants to share relevant information. Dietary Restrictions and Preferences: Asking, “Do you have any dietary restrictions or preferences that are culturally or religiously motivated?” ensures that these are respected. Community and Belonging: “Is there anything about your community or background that you would like us to know to provide better care?” encourages participants to share relevant aspects of their identity. These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. Ensuring Comfort, Trust, and Privacy Questions Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. Comfort and Confidentiality: Asking, “Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?” helps build trust. Feedback and Preferences: “Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?” invites participants to share their feedback. Consent and Voluntary Participation: Questions like, “Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary.” reinforce autonomy and respect. Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. By thoughtfully addressing both unique study requirements and universally relevant data points, researchers can optimize CRFs for consistency across clinical studies. 4.3.4 Clinical studies and trials Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions (“What Are Clinical Trials and Studies?” 2023). The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. Definition 4.1 The NIH’s definition of a clinical trial is “a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes.” The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study (“NIH’s Definition of a Clinical Trial,” n.d.). Definition 4.2 Prospectively assigned: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial (“NIH Clinical Trials,” n.d.). Here, we explore the different categories of questions that can be addressed using clinical trials data, ranging from the evaluation of treatment efficacy and safety to the exploration of predictive factors for disease prognosis and the personalization of medical care. Efficacy and Effectiveness Questions One of the primary objectives of clinical trials is to determine the efficacy of new treatments or interventions. Clinical data is used to answer fundamental questions such as “Is the new treatment effective?” and “How does its effectiveness compare to existing treatments?” Researchers design studies to measure the magnitude of treatment effects, comparing outcomes between treatment and control groups (e.g., a new drug versus a placebo or standard care). Beyond controlled settings, data from real-world evidence (RWE) studies can further address questions about the effectiveness of treatments when applied in routine clinical practice. For example, “Is Drug A more effective than Drug B in managing hypertension in a real-world patient population?” Such questions help to assess how treatments perform outside the controlled environment of clinical trials where patient adherence comorbidities, and polypharmacy may influence outcomes. While clinical trials are an important source of real-world data (RWD), they are typically conducted under tightly controlled conditions, which may not fully reflect the complexities of real-world studies. This makes RWE particularly valuable for understanding how treatments work when patients have varying levels of adherence or multiple chronic conditions that are often excluded from clinical trails. Definition 4.3 Comorbidities: refer to the presence of one or more additional medical conditions or diseases that coexist with a primary condition in a patient. These conditions can occur independently or be related to the primary disease. Safety and Tolerability Questions Safety is a critical aspect of any clinical study, and clinical data is essential in identifying and characterizing the safety profile of new treatments. Questions like “What are the common adverse effects of the new treatment?” or “What is the incidence of serious adverse events (SAEs) in patients receiving Drug A?” are addressed by carefully monitoring and recording adverse events throughout the study. Comparative safety questions also arise, such as “Does Drug A have fewer adverse effects compared to Drug B?” These questions are crucial in weighing the benefits of a treatment against its risk and in identifying specific populations that may be at higher risk of adverse reactions. Definition 4.4 Serious Adverse Events (SAEs): Any medical occurrence during a clinical trial or medical treatment that results in significant negative outcomes, such as death, life-threatening situations, hospitalization (or its prolongation), persistent or significant disability, or congenital anomalies. SAEs also include any event that requires intervention to prevent one of these outcomes. Definition 4.5 Incidence: Refers to the rate or number of new cases of SAEs that occur within a specific population during a defined time period, typically during a clinical trial or study. Is it usually expressed as a proportion, such as the number of SAEs per 100 or 1,000 participants, and helps measure how frequently these serious events happen among those exposed to a particular treatment or intervention. Comparative Effectiveness and Cost-Effectiveness Questions Beyond safety and efficacy, clinical data can be used to evaluate the comparative effectiveness of different treatments and their cost-effectiveness. These questions often guide healthcare policy and clinical guidelines: “Is Drug A more cost-effective than standard care for managing chronic heart failure?” and “What is the cost per quality-adjusted life year (QALY) gained with the new intervention?” Such analyses provide valuable information for stakeholders, including healthcare providers, payers, and policymakers, to make informed decisions on resource allocation. Definition 4.6 Cost per quality-adjusted life year (QALY) is a metric used in health economics to assess the value of a medical intervention by measuring the cost of gaining one year of life adjusted for its quality. A QALY incorporates both the quantity and quality of life lived, where one QALY is equivalent to one year of life in perfect health. If a treatment improves both the length and quality of life, it earns more QALYs. The cost per QALY is calculated by dividing the cost of the treatment by the number of QALYs gained, helping to determine the cost-effectiveness of healthcare interventions. Lower cost per QALY indicates better value for money in terms of health benefits achieved (2015). Mechanistic and Biomarker Questions Clinical studies often explore not just whether a treatment works, but also how it works. Mechanistic questions delve into the underlying biological pathways affected by a treatment. For instance, “Does Drug A work by inhibiting a specific enzyme involved in the disease process?” Biomarker data can play a key role in such analyses, helping to identify molecular markers that predict response to treatment. Questions like “Are there biomarkers that can predict which patients are more likely to benefit from Drug A?” help in developing targeted therapies and personalized treatment approaches. Predictive and Prognostic Questions Clinical trials and studies frequently aim to identify factors that predict disease outcomes or treatment responses. For example, “What baseline characteristics predict a better response to Drug A?” or “What are the predictors of mortality in patients with severe heart failure?” Such prognostic and predictive questions are crucial for identifying high-risk patients, guiding treatment decisions, and developing clinical guidelines. Identifying patient subgroups that benefit more or less from a treatment also facilitates personalized medicine approaches, ensuring that the right treatment is delivered to the right patient at the right time. With the advent of personalized medicine, clinical data is increasingly being used to answer questions tailored to individual patient profiles. For instance, “What patient characteristics modify the effect of the treatment?” or “Can we predict which patients are most likely to benefit from Drug A using machine learning models?” These questions are at the forefront of precision medicine, where the goal is to customize healthcare to each patient based on their unique genetic, biomarker, and clinical characteristics. Quality of Life and Patient-Reported Outcome Questions In modern clinical research, the impact of treatments on patients’ quality of life and their subjective experiences has gained prominence. Questions such as “How does Drug A affect patients’ quality of life compared to standard care?” or “What are the patient-reported outcomes associated with the new intervention?” are increasingly addressed in clinical trials. Data from quality of life assessments which are standardized and validated questionnaires (for example, the Health Assessment Questionnaire or HAQ), patient-reported outcome measures (PROMs, tools or questionnaires used to assess a patient’s health status or health-related quality of life directly from the patient’s perspective), and other patient-centric endpoints (Smoking History, Surgical History, etc.) provide valuable insights into how treatments affect patients beyond traditional clinical endpoints. Longitudinal and Follow-Up Questions Long-term follow-up studies are essential for understanding the durability of treatment effects and any delayed adverse effects. Questions like “What are the long-term outcomes associated with Drug A?” and “Is there a sustained benefit of Drug A in reducing symptoms after five years?” are vital for assessing the overall value of treatments. Such longitudinal analyses help in determining the optimal duration of therapy, the need for maintenance treatments, and the overall benefit-risk profile of an intervention. Safety in Special Populations Questions Clinical data can also be used to evaluate the safety and efficacy of treatments in special populations, such as children, pregnant women, or elderly patients. Questions such as “Is Drug A safe for use in pregnant women?” and “What is the risk of adverse events when Drug A is administered to patients with multiple comorbidities?” are essential for developing age- and condition-specific clinical guidelines. These questions help ensure that treatments are tailored to the needs of different patient populations, minimizing harm and maximizing benefits. Adherence and Compliance Questions Patient adherence to prescribed treatment regimens can significantly impact the outcomes of clinical studies. Questions like “What factors influence patient adherence to the new treatment?” and “How does adherence affect treatment efficacy?” are addressed through analyzing adherence data through monitors if a regulated study, or with interim/baseline analysis if non-regulated. Understanding these factors can lead to interventions that improve adherence and, consequently, the effectiveness of the treatment. Real-World Evidence and Generalizability Questions Real-world evidence (RWE) studies help bridge the gap between clinical trial results and clinical practice. Questions such as “How generalizable are the results of this clinical trial to the broader population?” or “What is the impact of real-world use patterns on treatment outcomes?” are essential for understanding how well clinical trial findings apply to everyday clinical settings. These questions help determine if the benefits observed in clinical trials can be replicated in diverse patient populations under routine care conditions. Prevention and Risk Reduction Questions Clinical data is also crucial in answering questions related to prevention and risk reduction strategies. For example, “Can the new treatment reduce the risk of developing diabetes in high-risk individuals?” or “What factors are associated with a reduced risk of cardiovascular disease in a large cohort study?” These questions are fundamental in preventive medicine, guiding public health interventions and informing clinical practice. Clinical Data provides a wealth of information that can be leveraged to answer a broad array of questions in clinical trials and studies. From understanding treatment efficacy and safety to exploring long-term outcomes, quality of life impacts, and the potential for personalized medicine, clinical data is foundational to advancing medical knowledge and improving patient care. Case report forms (CRFs) are instrumental in identifying patient cohorts or subgroups, documenting baseline characteristics, and capturing information on comorbidities, while also monitoring adherence data, which are essential for ensuring accurate and meaningful analysis. The type of questions that can be asked and answered are continually evolving as new data sources, analytical methods, and research paradigms emerge, further enriching the field of clinical research. 4.3.5 Retrospective analysis Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. Here, we explore the different categories of questions that can be addressed using retrospective data analysis: Descriptive Questions Descriptive questions aim to summarize and describe the characteristics of a patient population, disease, or healthcare process. They provide a foundational understanding of a dataset and are often the starting point for more complex analyses. Patient Demographics and Characteristics: What are the demographic profiles (age, gender, ethnicity, socioeconomic status) of patients diagnosed with a specific condition? What are the common comorbidities and risk factors in this population? Disease Prevalence and Incidence: What is the prevalence or incidence of a specific disease or condition in a particular population or geographic area over a defined period? Clinical Presentation: What are the most common presenting symptoms, clinical signs, or laboratory findings associated with a specific disease or condition? These questions help in understanding the baseline characteristics and epidemiology of diseases, which is crucial for planning further studies or developing public health interventions. Treatment and Intervention Questions Retrospective data can be used to examine the real-world effectiveness, safety, and patterns of treatment and interventions. These questions are critical for understanding how treatments are applied in clinical practice and their outcomes: Treatment Effectiveness: What is the effectiveness of a specific treatment or intervention in reducing symptoms, improving quality of life, or achieving clinical outcomes in a real-world setting? Adherence and Persistence: What are the rates of adherence and persistence with a prescribed treatment regimen over time? What factors are associated with higher or lower adherence rates? Comparative Effectiveness: How does the effectiveness of one treatment compare to another in a similar population? What are the relative benefits and risks associated with different treatment options? By examining historical data, researchers can gain insights into how different treatments perform outside the controlled environment of clinical trials, thereby informing clinical decision-making and guidelines. Outcome and Prognostic Questions Understanding patient outcomes and prognostic factors is central to retrospective analyses. These questions focus on the end results of healthcare practices and patient management, including survival, complications, and quality of life: Survival and Mortality: What are the survival rates and mortality rates associated with a particular disease, condition, or treatment? What factors are associated with increased or decreased survival rates? Complication Rates: What are the rates and types of complications associated with specific diseases, surgeries, or treatments? Are there identifiable risk factors for these complications? Prognostic Factors: What are the key prognostic factors (e.g., age, stage of disease, comorbidities) that influence the outcomes of patients with a particular condition? These questions help identify factors that influence patient outcomes, guiding clinicians in predicting disease progression and tailoring individual treatment plans. Risk Factor and Predictive Modeling Questions Retrospective analyses are often used to identify potential risk factors for diseases and develop predictive models. These questions aim to uncover associations between variables and outcomes to help predict future events: Risk Factors for Disease: What are the risk factors associated with developing a particular disease or condition? Are there demographic, genetic, behavioral, or environmental factors that significantly increase the risk? Prediction Models: Can we develop a predictive model to estimate the likelihood of disease progression, relapse, or adverse outcomes based on historical patient data? Multivariable Analysis: How do multiple factors interact to influence the risk of an outcome? Are there synergistic or antagonistic effects between different risk factors? These questions are critical for developing tools that help clinicians assess risk and make more informed decisions in preventive care and early intervention. Healthcare Utilization and Cost Questions Understanding how healthcare resources are utilized and the associated costs is important for optimizing healthcare delivery. Retrospective data can provide valuable insights into patterns of care and resource allocation for questions such as: Healthcare Resource Utilization: What are the patterns of healthcare utilization (e.g., hospitalizations, emergency room visits, outpatient visits) for patients with a specific condition? How does utilization vary by patient demographics, disease severity, or geographic location? Cost of Care: What are the direct and indirect costs associated with managing a specific disease or condition? How do different treatment options impact overall healthcare costs? Cost-Effectiveness: How does the cost-effectiveness of one treatment or intervention compare to another? What are the economic impacts of different clinical management strategies? These questions can inform healthcare policy, budget planning, and the development of strategies to improve cost-effectiveness and access to care. Quality of Care and Patient Safety Questions Retrospective analysis can be used to evaluate the quality and safety of care provided to patients, identifying areas for improvement and potential patient safety concerns, helping to answer questions such as: Quality Indicators: Quality indicators are standardized measures of health care quality and may focus on more specialized groups of care like preventative, inpatient or emergency care, pediatric or maternal health, etc. What are the levels of observed mortality or morbidity for a certain population in a specific setting? How utilized are certain procedures? (“AHRQ Quality Indicator Measures,” n.d.) Adverse Events and Safety: What is the incidence of adverse events, medication errors, or healthcare-associated infections in a particular patient population? What factors are associated with higher or lower rates of adverse events? Patient Satisfaction and Experience: What are the reported levels of patient satisfaction with healthcare services? How does satisfaction vary by demographic or clinical characteristics? These questions help in assessing healthcare quality, identifying gaps in care, and developing interventions to enhance patient safety and satisfaction. Temporal and Trend Analysis Questions Retrospective studies often involve analyzing temporal patterns to understand how diseases, treatments, or outcomes change over time. These questions help in recognizing trends and patterns that can inform future research and policy decisions: Temporal Trends in Disease Incidence and Prevalence: How have the incidence and prevalence of a specific disease changed over the past decade? Are there seasonal or cyclical patterns in disease occurrence? Trends in Treatment Practices: How have treatment practices and prescribing patterns evolved over time? Are there emerging trends in the use of certain medications or interventions? Trends in Outcomes and Survival: How have patient outcomes, such as survival rates or complication rates, changed over time with advances in treatment and healthcare delivery? These questions are essential for monitoring public health trends, evaluating the impact of interventions, and planning future healthcare strategies. Subgroup and Stratified Analysis Questions Subgroup analyses can help uncover differences in disease characteristics, treatment responses, and outcomes among different patient groups. This type of analysis is particularly valuable for personalized medicine and addressing questions related to health disparities: Differences by Demographic Group: How do treatment outcomes, risk factors, or disease characteristics differ between various demographic groups (e.g., age, gender, race/ethnicity)? Subgroup-Specific Effectiveness: Does a particular treatment work better in one subgroup of patients compared to another? What factors might explain these differences? Health Disparities: Are there disparities in healthcare access, treatment outcomes, or disease burden across different socioeconomic or minority groups? Questions focusing on subgroups are crucial for understanding variations in care and outcomes, identifying at-risk populations, and developing targeted interventions. Retrospective analysis of clinical data allows researchers to ask a wide range of questions that provide valuable insights into patient characteristics, disease epidemiology, treatment effectiveness, outcomes, risk factors, healthcare utilization, and more. By carefully formulating questions and analyzing historical data, researchers can uncover patterns, identify trends, and generate evidence that informs clinical practice, healthcare policy, and future research directions. This chapter outlines the various categories of questions that can be addressed through retrospective analysis, highlighting the potential of this approach to advance knowledge and improve patient care in diverse clinical settings. 4.4 Research Design Considerations Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, Information representativeness bias occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. Information presence bias, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. Treatment biases happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. Algorithm bias further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research (Andrew D. Boyd et al. 2023a; Andrew D. Boyd et al. 2023b): Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. 4.5 Conclusion References "],["clinical-data-management.html", "Chapter 5 Clinical Data Management 5.1 Learning Objectives 5.2 Clinical data handling tools 5.3 Importance of keeping clinical data safe 5.4 Government regulators 5.5 Documentation 5.6 Conclusion", " Chapter 5 Clinical Data Management 5.1 Learning Objectives 5.2 Clinical data handling tools 5.3 Importance of keeping clinical data safe 5.4 Government regulators 5.5 Documentation 5.6 Conclusion "],["appendix-i-ontologies-vocabularies-taxonomies-and-standards.html", "Chapter 6 Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards 6.1 SNOMED 6.2 ICD10 6.3 RxNorm 6.4 LOINC 6.5 Emerging standards such as FHIR 6.6 OMOP", " Chapter 6 Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards 6.1 SNOMED 6.2 ICD10 6.3 RxNorm 6.4 LOINC 6.5 Emerging standards such as FHIR 6.6 OMOP "],["about-the-authors.html", "About the Authors", " About the Authors These credits are based on our course contributors table guidelines.     Credits Names Pedagogy Lead Content Instructor(s) FirstName LastName Lecturer(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involved Delivered the course in some way - video or audio Content Author(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involved If any other authors besides lead instructor Content Contributor(s) (include section name/link in parentheses) - make new line if more than one section involved Jennifer Kelleher, Ph.D.1*; Abigail S. Robbertz, Ph.D. Content Editor(s)/Reviewer(s) Julia K. Herriott, B.S. 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Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medicationtaking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technologyrelated vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care.", - "accessed": { - "date-parts": [ - [ - 2025, - 4, - 22 - ] - ] - }, - "author": [ - { - "family": "Psihogios", - "given": "Alexandra M." - }, - { - "family": "King-Dowling", - "given": "Sara" - }, - { - "family": "Mitchell", - "given": "Jonathan A." - }, - { - "family": "McGrady", - "given": "Meghan E." - }, - { - "family": "Williamson", - "given": "Ariel A." - } - ], - "container-title": "American Psychologist", - "id": "psihogios_ethical_2024", - "issue": "1", - "issued": { - "date-parts": [ - [ - 2024, - 1 - ] - ] - }, - "page": "39-51", - "title": "Ethical considerations in using sensors to remotely assess pediatric health behaviors.", - "type": "article-journal", - "volume": "79" - }, - { - "URL": "https://division-research.brown.edu/research-cycle/proposal/agency-specific/nih/clinical-trials", - "container-title": "Division of Research at Brown University", - "id": "NIHClinicalTrialsProspectively", - "title": "NIH clinical trials", - "type": "" - }, - { - "URL": "https://grants.nih.gov/policy-and-compliance/policy-topics/clinical-trials/definition", - "id": "CTDefinition", - "title": "NIH’s definition of a clinical trial", - "type": "" - }, - { - "ISBN": "978-0-19-966588-4", - "author": [ - { - "family": "Drummond", - "given": "M. 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J." - }, - { - "family": "Grolemund", - "given": "Garrett" - } - ], - "id": "Xie2018", - "issued": { - "date-parts": [ - [ - 2018 - ] - ] - }, - "note": "ISBN 9781138359338", - "publisher": "Chapman; Hall/CRC", - "publisher-place": "Boca Raton, Florida", - "title": "R markdown: The definitive guide", - "title-short": "R markdown", - "type": "book" - }, - { - "URL": "https://bookdown.org/yihui/rmarkdown-cookbook", - "author": [ - { - "family": "Xie", - "given": "Yihui" - }, - { - "family": "Dervieux", - "given": "Christophe" - }, - { - "family": "Riederer", - "given": "Emily" - } - ], - "id": "Xie2020", - "issued": { - "date-parts": [ - [ - 2020 - ] - ] - }, - "note": "ISBN 9780367563837", - "publisher": "Chapman; Hall/CRC", - "publisher-place": "Boca Raton, Florida", - "title": "R markdown cookbook", - "type": "book" - }, - { - "DOI": "10.1037/amp0001196", - "ISSN": "1935-990X, 0003-066X", - "URL": "https://doi.apa.org/doi/10.1037/amp0001196", - "abstract": "Sensors, including accelerometer-based and electronic adherence monitoring devices, have transformed health data collection. Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medicationtaking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technologyrelated vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care.", - "accessed": { - "date-parts": [ - [ - 2025, - 4, - 22 - ] - ] - }, - "author": [ - { - "family": "Psihogios", - "given": "Alexandra M." - }, - { - "family": "King-Dowling", - "given": "Sara" - }, - { - "family": "Mitchell", - "given": "Jonathan A." - }, - { - "family": "McGrady", - "given": "Meghan E." - }, - { - "family": "Williamson", - "given": "Ariel A." - } - ], - "container-title": "American Psychologist", - "id": "psihogios_ethical_2024", - "issue": "1", - "issued": { - "date-parts": [ - [ - 2024, - 1 - ] - ] - }, - "page": "39-51", - "title": "Ethical considerations in using sensors to remotely assess pediatric health behaviors.", - "type": "article-journal", - "volume": "79" - }, - { - "DOI": "10.1111/j.1365-2125.2012.04167.x", - "ISSN": "0306-5251, 1365-2125", - "URL": "https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/j.1365-2125.2012.04167.x", - "abstract": "Interest in patient adherence has increased in recent years, with a growing literature that shows the pervasiveness of poor adherence to appropriately prescribed medications. However, four decades of adherence research has not resulted in uniformity in the terminology used to describe deviations from prescribed therapies. The aim of this review was to propose a new taxonomy, in which adherence to medications is conceptualized, based on behavioural and pharmacological science, and which will support quantifiable parameters. A systematic literature review was performed using MEDLINE, EMBASE, CINAHL, the Cochrane Library and PsycINFO from database inception to 1 April 2009. The objective was to identify the different conceptual approaches to adherence research. Definitions were analyzed according to time and methodological perspectives. A taxonomic approach was subsequently derived, evaluated and discussed with international experts. More than 10 different terms describing medication‐taking behaviour were identified through the literature review, often with differing meanings. The conceptual foundation for a new, transparent taxonomy relies on three elements, which make a clear distinction between processes that describe actions through established routines (“Adherence to medications”, “Management of adherence”) and the discipline that studies those processes (“Adherence‐related sciences”). “Adherence to medications” is the process by which patients take their medication as prescribed, further divided into three quantifiable phases: “Initiation”, “Implementation” and “Discontinuation”. In response to the proliferation of ambiguous or unquantifiable terms in the literature on medication adherence, this research has resulted in a new conceptual foundation for a transparent taxonomy. The terms and definitions are focused on promoting consistency and quantification in terminology and methods to aid in the conduct, analysis and interpretation of scientific studies of medication adherence.", - "accessed": { - "date-parts": [ - [ - 2025, - 4, - 22 - ] - ] - }, - "author": [ - { - "family": "Vrijens", - "given": "Bernard" - }, - { - "family": "De Geest", - "given": "Sabina" - }, - { - "family": "Hughes", - "given": "Dyfrig A." - }, - { - "family": "Przemyslaw", - "given": "Kardas" - }, - { - "family": "Demonceau", - "given": "Jenny" - }, - { - "family": "Ruppar", - "given": "Todd" - }, - { - "family": "Dobbels", - "given": "Fabienne" - }, - { - "family": "Fargher", - "given": "Emily" - }, - { - "family": "Morrison", - "given": "Valerie" - }, - { - "family": "Lewek", - "given": "Pawel" - }, - { - "family": "Matyjaszczyk", - "given": "Michal" - }, - { - "family": "Mshelia", - "given": "Comfort" - }, - { - "family": "Clyne", - "given": "Wendy" - }, - { - "family": "Aronson", - "given": "Jeffrey K." - }, - { - "family": "Urquhart", - "given": "J." - }, - { - "literal": "for the ABC Project Team" - } - ], - "container-title": "British Journal of Clinical Pharmacology", - "id": "vrijens_new_2012", - "issue": "5", - "issued": { - "date-parts": [ - [ - 2012, - 5 - ] - ] - }, - "page": "691-705", - "title": "A new taxonomy for describing and defining adherence to medications", - "type": "article-journal", - "volume": "73" - }, - { - "DOI": "10.1542/peds.2011-1635", - "ISSN": "0031-4005, 1098-4275", - "URL": "https://publications.aap.org/pediatrics/article/129/2/e473/32549/Pediatric-Self-management-A-Framework-for-Research", - "abstract": "Self-management of chronic pediatric conditions is a formidable challenge for patients, families, and clinicians, with research demonstrating a high prevalence of poor self-management and nonadherence across pediatric conditions. Nevertheless, effective self-management is necessary to maximize treatment efficacy and clinical outcomes and to reduce unnecessary health care utilization and costs. However, this complex behavior is poorly understood as a result of insufficient definitions, reliance on condition-specific and/or adult models of self-management, failure to consider the multitude of factors that influence patient self-management behavior, and lack of synthesis of research, clinical practice, and policy implications. To address this need, we present a comprehensive conceptual model of pediatric self-management that articulates the individual, family, community, and health care system level influences that impact self-management behavior through cognitive, emotional, and social processes. This model further describes the relationship among self-management, adherence, and outcomes at both the patient and system level. Implications for research, clinical practice, and health care policy concerning pediatric chronic care are emphasized with a particular focus on modifiable influences, evidence-based targets for intervention, and the role of clinicians in the provision of self-management support. We anticipate that this unified conceptual approach will equip stakeholders in pediatric health care to (1) develop evidence-based interventions to improve self-management, (2) design programs aimed at preventing the development of poor self-management behaviors, and (3) inform health care policy that will ultimately improve the health and psychosocial outcomes of children with chronic conditions.", - "accessed": { - "date-parts": [ - [ - 2025, - 4, - 22 - ] - ] - }, - "author": [ - { - "family": "Modi", - "given": "Avani C." - }, - { - "family": "Pai", - "given": "Ahna L." - }, - { - "family": "Hommel", - "given": "Kevin A." - }, - { - "family": "Hood", - "given": "Korey K." - }, - { - "family": "Cortina", - "given": "Sandra" - }, - { - "family": "Hilliard", - "given": "Marisa E." - }, - { - "family": "Guilfoyle", - "given": "Shanna M." - }, - { - "family": "Gray", - "given": "Wendy N." - }, - { - "family": "Drotar", - "given": "Dennis" - } - ], - "container-title": "Pediatrics", - "id": "modi_pediatric_2012", - "issue": "2", - "issued": { - "date-parts": [ - [ - 2012, - 2 - ] - ] - }, - "page": "e473-e485", - "title": "Pediatric Self-management: A Framework for Research, Practice, and Policy", - "title-short": "Pediatric Self-management", - "type": "article-journal", - "volume": "129" - }, - { - "URL": "https://www.R-project.org/", - "author": [ - { - "literal": "R Core Team" - } - ], - "id": "R-base", - "issued": { - "date-parts": [ - [ - 2020 - ] - ] - }, - "publisher": "R Foundation for Statistical Computing", - "publisher-place": "Vienna, Austria", - "title": "R: A language and environment for statistical computing", - "title-short": "R", - "type": "book" - }, - { - "author": [ - { - "family": "Xie", - "given": "Yihui" - } - ], - "id": "R-bookdown", - "issued": { - "date-parts": [ - [ - 2021 - ] - ] - }, - "note": "https://github.com/rstudio/bookdown, https://pkgs.rstudio.com/bookdown/", - "title": "Bookdown: Authoring books and technical documents with r markdown", - "title-short": "Bookdown", - "type": "book" - }, - { - "URL": "https://yihui.org/knitr/", - "author": [ - { - "family": "Xie", - "given": "Yihui" - } - ], - "id": "R-knitr", - "issued": { - "date-parts": [ - [ - 2021 - ] - ] - }, - "note": "R package version 1.33", - "title": "Knitr: A general-purpose package for dynamic report generation in r", - "title-short": "Knitr", - "type": "book" - }, - { - "author": [ - { - "family": "Allaire", - "given": "JJ" - }, - { - "family": "Xie", - "given": "Yihui" - }, - { - "family": "McPherson", - "given": "Jonathan" - }, - { - "family": "Luraschi", - "given": "Javier" - }, - { - "family": "Ushey", - "given": "Kevin" - }, - { - "family": "Atkins", - "given": "Aron" - }, - { - "family": "Wickham", - "given": "Hadley" - }, - { - "family": "Cheng", - "given": "Joe" - }, - { - "family": "Chang", - "given": "Winston" - }, - { - "family": "Iannone", - "given": "Richard" - } - ], - "id": "R-rmarkdown", - "issued": { - "date-parts": [ - [ - 2021 - ] - ] - }, - "note": "https://github.com/rstudio/rmarkdown, https://pkgs.rstudio.com/rmarkdown/", - "title": "Rmarkdown: Dynamic documents for r", - "title-short": "Rmarkdown", - "type": "book" - }, - { - "URL": "https://bookdown.org/yihui/bookdown", - "author": [ - { - "family": "Xie", - "given": "Yihui" - } - ], - "id": "bookdown2016", - "issued": { - "date-parts": [ - [ - 2016 - ] - ] - }, - "note": "ISBN 978-1138700109", - "publisher": "Chapman; Hall/CRC", - "publisher-place": "Boca Raton, Florida", - "title": "Bookdown: Authoring books and technical documents with R markdown", - "title-short": "Bookdown", - "type": "book" - }, - { - "URL": "https://yihui.org/knitr/", - "author": [ - { - "family": "Xie", - "given": "Yihui" - } - ], - "edition": "2nd", - "id": "knitr2015", - "issued": { - "date-parts": [ - [ - 2015 - ] - ] - }, - "note": "ISBN 978-1498716963", - "publisher": "Chapman; Hall/CRC", - "publisher-place": "Boca Raton, Florida", - "title": "Dynamic documents with R and knitr", - "type": "book" - }, - { - "URL": "http://www.crcpress.com/product/isbn/9781466561595", - "author": [ - { - "family": "Xie", - "given": "Yihui" - } - ], - "container-title": "Implementing reproducible computational research", - "editor": [ - { - "family": "Stodden", - "given": "Victoria" - }, - { - "family": "Leisch", - "given": "Friedrich" - }, - { - "family": "Peng", - "given": "Roger D." - } - ], - "id": "knitr2014", - "issued": { - "date-parts": [ - [ - 2014 - ] - ] - }, - "note": "ISBN 978-1466561595", - "publisher": "Chapman; Hall/CRC", - "title": "Knitr: A comprehensive tool for reproducible research in R", - "title-short": "Knitr", - "type": "chapter" - }, - { - "URL": "https://bookdown.org/yihui/rmarkdown", - "author": [ - { - "family": "Xie", - "given": "Yihui" - }, - { - "family": "Allaire", - "given": "J. 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diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/01-data_types.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/01-data_types.Rmd.xref deleted file mode 100644 index 64429a4..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/01-data_types.Rmd.xref +++ /dev/null @@ -1,11 +0,0 @@ -h1:clinical-data-types Clinical Data Types -h2:learning-objectives Learning Objectives -h2:clinical-data-is-unique Clinical data is unique -h2:major-clinical-data-types Major clinical data types -h3:structured-data Structured data -h3:unstructured-data-clinical-notes Unstructured data / clinical notes -h2:description-of-data Description of data -h3:file-types File types -h3:metadata Metadata -h2:summary Summary - diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/01b-specific_data_types.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/01b-specific_data_types.Rmd.xref deleted file mode 100644 index 4899754..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/01b-specific_data_types.Rmd.xref +++ /dev/null @@ -1,12 +0,0 @@ -h1:specific-clinical-data-types Specific Clinical Data Types -h2:learning-objectives Learning Objectives -h2:physiological Physiological -h2:monitoring-data Monitoring data -h3:benefits-of-monitoring-data Benefits of Monitoring Data -h3:considerations Considerations -h4:medication-adherence Medication Adherence -h2:radiology Radiology -h2:pathology Pathology -h2:synthetic-data Synthetic Data -h2:summary Summary - diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/01c-how_to_collect_data.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/01c-how_to_collect_data.Rmd.xref deleted file mode 100644 index 3c8040e..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/01c-how_to_collect_data.Rmd.xref +++ /dev/null @@ -1,7 +0,0 @@ -h1:how-to-collect-clinical-data How to Collect Clinical Data -h2:learning-objectives Learning Objectives -h2:case-report-forms-crfs Case Report Forms (CRFs) -h2:clinical-studies-and-trials Clinical Studies and Trials -h2:research-design-considerations Research Design Considerations -h2:summary Summary - diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/01d-how_to_acquire_data.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/01d-how_to_acquire_data.Rmd.xref deleted file mode 100644 index 695548e..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/01d-how_to_acquire_data.Rmd.xref +++ /dev/null @@ -1,9 +0,0 @@ -h1:how-to-acquire-clinical-data How to Acquire Clinical Data -h2:learning-objectives Learning Objectives -h2:electronic-health-records Electronic Health Records -h2:medical-databases Medical Databases (?) -h2:registries Registries (?) -h2:secondary-sources Secondary Sources -h2:retrospective-analyses Retrospective Analyses -h2:summary Summary - diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/02-data_uses.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/02-data_uses.Rmd.xref deleted file mode 100644 index be005b8..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/02-data_uses.Rmd.xref +++ /dev/null @@ -1,12 +0,0 @@ -h1:clinical-data-uses Clinical Data Uses -h2:learning-objectives Learning Objectives -h2:general-uses-of-clinical-data General uses of clinical data -h3:cancer-prevention-and-care Cancer Prevention and Care -h2:types-of-questions-that-can-be-asked-with-clinical-data Types of questions that can be asked with clinical data -h3:risk-prediction Risk Prediction -h3:cohort-identification-for-research Cohort identification for research -h3:clinical-studies-and-trials Clinical studies and trials -h3:retrospective-analysis Retrospective analysis -h2:research-design-considerations Research Design Considerations -h2:conclusion Conclusion - diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/03-data_management.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/03-data_management.Rmd.xref deleted file mode 100644 index 4b31a99..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/03-data_management.Rmd.xref +++ /dev/null @@ -1,8 +0,0 @@ -h1:clinical-data-management Clinical Data Management -h2:learning-objectives Learning Objectives -h2:clinical-data-handling-tools Clinical data handling tools -h2:importance-of-keeping-clinical-data-safe Importance of keeping clinical data safe -h2:government-regulators Government regulators -h2:documentation Documentation -h2:conclusion Conclusion - diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/04-appendixI.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/04-appendixI.Rmd.xref deleted file mode 100644 index 0447db7..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/04-appendixI.Rmd.xref +++ /dev/null @@ -1,8 +0,0 @@ -h1:appendix-i-ontologies-vocabularies-taxonomies-and-standards Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards -h2:snomed SNOMED -h2:icd10 ICD10 -h2:rxnorm RxNorm -h2:loinc LOINC -h2:emerging-standards-such-as-fhir Emerging standards such as FHIR -h2:omop OMOP - diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/About.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/About.Rmd.xref deleted file mode 100644 index 4d55ec0..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/About.Rmd.xref +++ /dev/null @@ -1,2 +0,0 @@ -h1:about-the-authors About the Authors - diff --git a/.Rproj.user/6F8C099B/bookdown-crossref/References.Rmd.xref b/.Rproj.user/6F8C099B/bookdown-crossref/References.Rmd.xref deleted file mode 100644 index d24a95a..0000000 --- a/.Rproj.user/6F8C099B/bookdown-crossref/References.Rmd.xref +++ /dev/null @@ -1,2 +0,0 @@ -h1:references References - 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- -```{r include=FALSE} -# automatically create a bib database for R packages -knitr::write_bib(c( - .packages(), "bookdown", "knitr", "rmarkdown" -), "packages.bib") -``` - -# About this Course {-} - -This course is part of a series of courses for the [Informatics Technology for Cancer Research (ITCR)](https://itcr.cancer.gov/) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR Training Network (ITN) which is a collaborative effort of researchers around the United States to support cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: [National Cancer Institute (NCI)](https://www.cancer.gov/) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our website at [www.itcrtraining.org](www.itcrtraining.org) for more information. diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/3E83D136-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/3E83D136-contents deleted file mode 100644 index ad7e3b4..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/3E83D136-contents +++ /dev/null @@ -1,551 +0,0 @@ -@import url('https://fonts.googleapis.com/css?family=Abril+Fatface|Karla:400,400i,700,700i|Lora:400,400i,700,700i&display=swap'); - -p.caption { - color: #777; - margin-top: 10px; -} -p code { - white-space: inherit; -} -pre { - word-break: normal; - word-wrap: normal; -} -pre code { - white-space: inherit; -} - -/*---------------Hero Image------------------*/ - -/* next 3 rules are for setting large image at top of each page and pushing down the book content to appear beneath the image */ - -.hero-image-container { - position: absolute; - top: 0; - left: 0; - right: 0; - height: 200px; - /*background-image: url("images/books.jpg");*/ - /*background-color: #193a5c;*/ -} - -.hero-image { - width: 100%; - height: 80%; - /*object-fit: cover;*/ -} - -.page-inner { - padding-top: 140px !important; -} - - -/* ------------Links------------------ */ - -.book .book-body .page-wrapper .page-inner section.normal a { - color: #e0471c; -} - - -/*------------- Body and header text---------------- */ - -.book.font-family-1 { - font-family: 'Karla', arial, sans-serif; -} - -h1, h2, h3, h4 { - font-family: 'Lora', arial, sans-serif; -} - - -.book .book-body .page-wrapper .page-inner section.normal h1, -.book .book-body .page-wrapper .page-inner section.normal h2, -.book .book-body .page-wrapper .page-inner section.normal h3, -.book .book-body .page-wrapper .page-inner section.normal h4, -.book .book-body .page-wrapper .page-inner section.normal h5, -.book .book-body .page-wrapper .page-inner section.normal h6 { - margin-top: 2.275em; - margin-bottom: 1em; -} - -.title { - font-family: 'Lora'; - font-size: 4em !important; - color: #193a5c; - margin-top: 0.275em !important; - margin-bottom: 0.35em !important; -} - -.subtitle { - font-family: 'Lora'; - color: #0b8d96; -} - - -/*----------DROP CAPS--------------*/ - - -/*p:nth-child(2):first-letter { /* /* DROP-CAP FOR FIRST P BENEATH EACH H1 OR H2*/ /* - color: #3fb5bd; - float: left; - font-family: 'Abril Fatface', serif; - font-size: 7em; - line-height: 65px; - padding-top: 4px; - padding-right: 8px; - padding-left: 3px; - margin-bottom: 9px; -} -*/ - -.section.level1 > p:first-of-type:first-letter { /*drop cap for first p beneath level 1 headers only within class .section*/ - color: #193a5c; - float: left; - font-family: 'Abril Fatface', serif; - font-size: 7em; - line-height: 65px; - padding-top: 4px; - padding-right: 8px; - padding-left: 3px; - margin-bottom: 9px; -} - -/* add drop cap to first paragraph that follows the first 2nd level header*/ -/* -.section.level2:first-of-type > p:first-of-type:first-letter { - color: #3fb5bd; - float: left; - font-family: 'Abril Fatface', serif; - font-size: 7em; - line-height: 65px; - padding-top: 4px; - padding-right: 8px; - padding-left: 3px; - margin-bottom: 9px; -} -*/ - - - -/*------------ TOC --------------*/ - - -.book .book-summary { - background: white; - border-right: none; -} - -/*---color of links in TOC----*/ - -.book .book-summary a { -color: #193a5c -} - -.summary{ - font-family: 'Karla', sans-serif; -} - -/* all TOC list items, basically */ -.book .book-summary ul.summary li a, -.book .book-summary ul.summary li span { - padding-top: 8px; - padding-bottom: 8px; - padding-left: 15px; - padding-right: 15px; - color: #193a5c; -} - -.summary a:hover { - color: #e0471c !important; -} - -.book .book-summary ul.summary li.active>a { /*active TOC links*/ - color: #e0471c !important; - border-left: solid 4px; - border-color: #e0471c; - padding-left: 11px !important; -} - -.trapezoid { - width: 130px; - text-align: center; - height: 15; - position: relative; - border-right: 50px solid transparent; - border-bottom: 50px solid #dbdbdb; - border-left: 50px solid transparent; - box-sizing: content-box; -} -.trapezoid span { - position: absolute; - top: 5px; - bottom: 5px; - left: 5%; - color: #fff; -} - - - - -li.appendix span, li.part span { /* for TOC part names */ - margin-top: 1em; - color: #000000; - opacity: .9 !important; - text-transform: uppercase; -} - -.part + li[data-level=""] { /* grabs first .chapter immediately after .part...but only those ch without numbers */ - text-transform: uppercase; -} - - -/* The next two rules make the horizontal line go straight across in top navbar */ - -.summary > li:first-child { - height: 50px; - padding-top: 10px; - border-bottom: 1px solid rgba(0,0,0,.07); -} - -.book .book-summary ul.summary li.divider { - height: 0px; -} - - - -/*--- LOGO ---*/ - -.toc-logo { - width: 200px !important; - object-fit: contain; - margin: 0 auto; -} - -.toc-logo img { - max-width: 100%; - margin-bottom: 10px; -} - -.summary > li:first-child { - height: auto !important; -} - -/* --------------Two columns--------------- */ - -.col2 { - columns: 2 200px; /* number of columns and width in pixels*/ - -webkit-columns: 2 200px; /* chrome, safari */ - -moz-columns: 2 200px; /* firefox */ -} - - -.side-by-side { - display: flex; -} - -.side1 { - width: 40%; -} - -.side2 { - width: 58%; - margin-left: 1rem; -} - -/* --------------Even split- two columns--------------- */ - -.split { - display: flex; -} - -.split1 { - width: 50%; - padding-right: 20px; -} - -.split2 { - width: 50%; -} - - -/* Sidebar formating --------------------------------------------*/ -/* from r-pkgs.org*/ - -div.notice{ - border: 4px #193a5c; - border-style: solid; - padding: 1em; - margin: 1em 0; - padding-left: 100px; - background-size: 70px; - background-repeat: no-repeat; - background-position: 15px center; - min-height: 120px; - background-color: #e8ebee; -} - -div.notice{ - background-image: url("../assets/box_images/note.png"); -} - -div.warning{ - border: 4px #e0471c; - border-style: solid; - padding: 1em; - margin: 1em 0; - padding-left: 100px; - background-size: 70px; - background-repeat: no-repeat; - background-position: 15px center; - min-height: 120px; - background-color: #e8ebee; -} - -div.warning{ - background-image: url("../assets/box_images/warning.png"); -} - - - -div.github{ - border: 4px #000000; - border-style: solid; - padding: 1em; - margin: 1em 0; - padding-left: 100px; - background-size: 70px; - background-repeat: no-repeat; - background-position: 15px center; - min-height: 120px; - background-color: #e8ebee; -} - -div.github{ - background-image: url("../assets/box_images/github.png"); -} - -div.dictionary{ - border: 4px #193a5c; - border-style: solid; - padding: 1em; - margin: 1em 0; - padding-left: 100px; - background-size: 70px; - background-repeat: no-repeat; - background-position: 15px center; - min-height: 120px; - background-color: #e8ebee; -} - -div.dictionary{ - background-image: url("../assets/box_images/dictionary.png"); -} - -div.reflection{ - border: 4px #193a5c; - border-style: solid; - padding: 1em; - margin: 1em 0; - padding-left: 100px; - background-size: 90px; - background-repeat: no-repeat; - background-position: 15px center; - min-height: 120px; - background-color: #e8ebee; -} - -div.reflection{ - background-image: url("../assets/box_images/thinking_face.png"); -} - - -div.rstudio-tip, div.tip, div.gotcha, div.design, div.hat{ - border: 4px #aed6d963; - border-style: dashed solid; - padding: 1em; - margin: 1em 0; - padding-left: 100px; - background-size: 70px; - background-repeat: no-repeat; - background-position: 15px center; - min-height: 120px; - color: #2ea8b3; - background-color: #fcfcfc; -} - - -div.rstudio-tip { - background-image: url("../images/divs/rstudio.png"); -} - -div.tip { - background-image: url("../images/divs/lightbulb.png"); -} - -div.gotcha { - background-image: url("../images/divs/gotcha_2.png"); -} - -div.design { - background-image: url("../images/divs/design.png"); -} - -div.hat { - background-image: url("../images/divs/hat.png"); -} - -/* for fancy bookdown cookbook */ - -div.puzzle, div.fyi, div.demo, div.note { - padding: 1em; - margin: 1em 0; - padding-left: 100px; - background-size: 70px; - background-repeat: no-repeat; - background-position: 15px center; - min-height: 120px; - color: #1f5386; - background-color: #bed3ec; - border: solid 5px #dfedff; -} - -div.puzzle { - background-image: url("../images/illos/Your-turn.png"); -} - -div.fyi { - background-image: url("../images/illos/fyi.png"); -} - -div.demo { - background-image: url("../images/illos/Live-code.png"); -} - -div.note { - background-image: url("../images/illos/lightbulb2.png"); -} - - -/* .book .book-body .page-wrapper .page-inner section.normal is needed - to override the styles produced by gitbook, which are ridiculously - overspecified. Goal of the selectors is to ensure internal "margins" - controlled only by padding of container */ - -.book .book-body .page-wrapper .page-inner section.normal div.rstudio-tip > :first-child, -.book .book-body .page-wrapper .page-inner section.normal div.tip > :first-child { - margin-top: 0; -} - -.book .book-body .page-wrapper .page-inner section.normal div.rstudio-tip > :last-child, -.book .book-body .page-wrapper .page-inner section.normal div.tip > :last-child { - margin-bottom: 0; -} - -iframe { - -moz-transform-origin: top left; - -webkit-transform-origin: top left; - -o-transform-origin: top left; - -ms-transform-origin: top left; - transform-origin: top left; -} - -.iframe-container { - overflow: auto; - -webkit-overflow-scrolling: touch; - border: #ddd 2px solid; - box-shadow: #888 0px 5px 8px; - margin-bottom: 1em; -} - -.iframe-container > iframe { - border: none; -} - - -/* -------Clipboard copy button --------- */ -.copy { - width: inherit; - background-color: #e2e2e2 ; - border: none; - border-radius: 2px; - float: right; - font-size: 60%; - padding: 4px 4px 4px 4px; -} - -/* for new copy button */ - -.hasCopyButton { - position: relative; -} - -.btn-copy-ex { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.hasCopyButton:hover button.btn-copy-ex { - visibility: visible; -} - - - -/* -----------Section anchors -------------*/ - -.book .book-body .page-wrapper .page-inner section.normal { - overflow: visible !important; /*so anchor link doesn't get cut off */ -} - -a.anchor { - margin-left: -30px; - padding-right: 3px; - display:inline-block; - width: 30px; - height: 30px; - /*visibility: hidden;*/ - background-image: url(https://image.flaticon.com/icons/svg/34/34735.svg) !important; - background-repeat: no-repeat !important; - background-size: 20px 20px !important; - background-position: center bottom !important; - opacity: 0; -} - - -.hasAnchor:hover a.anchor, -a.anchor:hover { - /*visibility: visible;*/ - opacity: 0.6; -} - -/* disable anchors for headers with "no-anchor" class */ -.no-anchor .hasAnchor:hover a.anchor, -.no-anchor a.anchor:hover { - opacity: 0 !important; -} - -@media (max-width: 1144px) { /* don't show anchor link on small screens */ - .hasAnchor:hover a.anchor { - visibility: hidden; - } -} - - -/*____Code Chunks____*/ - -.sourceCode { - margin-bottom: 0.85em; /* adds same amount of margin as a

would */ -} - -.sourceCode pre { - margin: 0 !important; /* makes space above and below symmetrical*/ -} - -/* Footer */ - -.footer { - font-family: "Lora", serif; - font-size: .85em; - color: #193a5c; -} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2 b/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2 deleted file mode 100644 index e2f9692..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2 +++ /dev/null @@ -1,26 +0,0 @@ -{ - "id": "3F1840A2", - "path": "~/Documents/clinical_data_course/Clinical_Data_Management/01d-how_to_acquire_data.Rmd", - "project_path": "01d-how_to_acquire_data.Rmd", - "type": "r_markdown", - "hash": "0", - "contents": "", - "dirty": false, - "created": 1781803495658.0, - "source_on_save": false, - "relative_order": 4, - "properties": { - "source_window_id": "", - "Source": "Source", - "cursorPosition": "14,55", - "scrollLine": "2" - }, - "folds": "", - "lastKnownWriteTime": 1781805265, - "encoding": "UTF-8", - "collab_server": "", - "source_window": "", - "last_content_update": 1781805265532, - "read_only": false, - "read_only_alternatives": [] -} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2-contents deleted file mode 100644 index 74fe804..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/3F1840A2-contents +++ /dev/null @@ -1,19 +0,0 @@ -# How to Acquire Clinical Data - -## Learning Objectives - -## Electronic Health Records - -## Medical Databases (?) - -## Registries (?) - -## Secondary Sources - -## Retrospective Analyses - -Does this belong in collecting data or acquiring data? - -Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. - -## Summary diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89 b/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89 deleted file mode 100644 index 1b27c38..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89 +++ /dev/null @@ -1,26 +0,0 @@ -{ - "id": "6FE7FC89", - "path": "~/Documents/clinical_data_course/Clinical_Data_Management/01-data_types.Rmd", - "project_path": "01-data_types.Rmd", - "type": "r_markdown", - "hash": "3828264916", - "contents": "", - "dirty": false, - "created": 1781803614828.0, - "source_on_save": false, - "relative_order": 5, - "properties": { - "source_window_id": "", - "Source": "Source", - "cursorPosition": "72,635", - "scrollLine": "69" - }, - "folds": "", - "lastKnownWriteTime": 1781803626, - "encoding": "UTF-8", - "collab_server": "", - "source_window": "", - "last_content_update": 1781803626629, - "read_only": false, - "read_only_alternatives": [] -} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89-contents deleted file mode 100644 index afa93af..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/6FE7FC89-contents +++ /dev/null @@ -1,82 +0,0 @@ ---- -title: Clinical Data Types ---- - -```{r, include = FALSE} -ottrpal::set_knitr_image_path() -``` - - -# Clinical Data Types - -Clinical data is health-related information collected from patients throughout their healthcare journey. It may come in many forms and its sensitive nature requires careful management by researchers. - -## Learning Objectives - -```{r, fig.align='center', echo = FALSE, fig.alt= "Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data", out.width="100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g3385bea4ad0_0_30" ) -``` - - - -## Clinical data is unique - -Clinical data refers to information __collected from patients__ during healthcare delivery, clinical trials, and medical research. Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data with predictable and consistent formats, clinical data is often heterogeneous, integrating many forms of both structured and unstructured data: quantitative measurements, categorical data, genotyping, images, subjective narratives from patient notes, and objective observations or conclusions. The unstructured nature of free text from notes reflecting subjective patient experiences or qualitative insights from healthcare professionals especially adds a further layer of complexity. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data's unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare. Further, because clinical data contains sensitive, personal information about patients, there are additional security and ethics concerns in the handling and management of clinical data. - -## Major clinical data types - -Clinical data can come in many different forms, including - -* patient demographics -* medical history or records such as diagnoses, lab results, vital signs, medication records, or procedure history -* genetic reports -* health monitor data -* images -* scanned documents, and notes written by physicians, nurses, and other clinicians -* survey/ case report form (CRF responses) -* and more ... - -```{r fig.align='center', echo = FALSE, fig.alt= "Clinical data refers to information collected from patients during healthcare delivery, clinical trials, and medical research and could include demographics, medical history, lab results, imaging, treatment outcomes, and more", out.width="100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.g35bf5e18bfe_0_0#slide=id.g35bf5e18bfe_0_0") -``` - -Some sources of clinical data are more prevalent and readily obtainable than others. For instance, notes, demographics, images, and histories or observations/records (lab results, vitals, medications, procedures) are often stored directly in electronic medical record systems making them more easily accessible and so are the focus of most Electronic Health Record (EHR) data research efforts. - -### Structured data - -Observational records such as test results and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Structured data types frequently used in EHR research consist of comprehensive longitudinal records of a patient's interactions with a healthcare system and may include demographics, diagnoses, lab values, procedures, vitals, and medication records. - -```{r, fig.align='center', echo = FALSE, fig.alt= "Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. ", out.width="100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g338f75828af_0_7" ) -``` - -The structured nature of this data allows for it to be stored in tables which may be indexed by a patient or visit ID and often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. - -Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or "billing codes", provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. - -```{r, fig.align='center', echo = FALSE, fig.alt= "Example of structured data, a table that includes patient id numbers, billing codes, dates, blood pressure measurements, weight measurements, height measurements, and prescribed medications coded using the National Drug Code (NDC)", out.width="100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g3385bea4ad0_0_0" ) -``` - -As we've described it, structured clinical data is expected to have similarities, although specific details may vary based on the type of EHR being used and any customizations to the EHR for the specific environment or institution in which data was collected (e.g., any specialized pre-processing by institutional research offices prior to providing data to researchers). So it is important for researchers to consider differences present in their data if they've obtained it from multiple contexts or institutions. - -### Unstructured data / clinical notes - -Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. - - -```{r, fig.align='center', echo = FALSE, fig.alt= "Unstructured Data - Data without specific format: includes images, pathology reports, radiology reports, clinical notes, discharge summaries and more. Includes an image of an x-ray and some clinical notes that states: Patient reports a sharp pain in the right lower abdomen for the past 24 hours.Temperature 98.6°F, BP 120/80, tenderness in the right lower quadrant. Suspected appendicitis. Refer to surgery for evaluation, initiate IV fluids, and pain management.", out.width="100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g3385bea4ad0_0_14" ) -``` - - -When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [@Dhir2008] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. - -## Description of data - -### File types - -### Metadata - - -## Summary diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/71A103E4-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/71A103E4-contents deleted file mode 100644 index 011f3e2..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/71A103E4-contents +++ /dev/null @@ -1,21 +0,0 @@ ---- -title: Clinical Data Management ---- - -```{r, include = FALSE} -ottrpal::set_knitr_image_path() -``` - -# Clinical Data Management - -## Learning Objectives - -## Clinical data handling tools - -## Importance of keeping clinical data safe - -## Government regulators - -## Documentation - -## Conclusion \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF b/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF deleted file mode 100644 index 3b82548..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF +++ /dev/null @@ -1,26 +0,0 @@ -{ - "id": "7D432FCF", - "path": "~/Documents/clinical_data_course/Clinical_Data_Management/_bookdown.yml", - "project_path": "_bookdown.yml", - "type": "yaml", - "hash": "3764396951", - "contents": "", - "dirty": false, - "created": 1781803327810.0, - "source_on_save": false, - "relative_order": 2, - "properties": { - "source_window_id": "", - "Source": "Source", - "cursorPosition": "8,40", - "scrollLine": "0" - }, - "folds": "", - "lastKnownWriteTime": 1781803435, - "encoding": "UTF-8", - "collab_server": "", - "source_window": "", - "last_content_update": 1781803435517, - "read_only": false, - "read_only_alternatives": [] -} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF-contents deleted file mode 100644 index 9eb9ae1..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/7D432FCF-contents +++ /dev/null @@ -1,21 +0,0 @@ -book_filename: "Course_Name" -chapter_name: "Chapter " -repo: https://github.com/jhudsl/DaSL_Course_Template_Bookdown/ -rmd_files: ["index.Rmd", - "00-intro.Rmd", - "01-data_types.Rmd", - "01b-specific_data_types.Rmd", - "01c-how_to_collect_data.Rmd", - "01d-how_to_acquire_data.Rmd" - "02-data_uses.Rmd", - "03-data_management.Rmd", - "04-appendixI.Rmd", - "About.Rmd", - "References.Rmd"] -new_session: yes -bibliography: [book.bib, packages.bib] -delete_merged_file: true -language: - ui: - chapter_name: "Chapter" -output_dir: "docs" diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF b/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF deleted file mode 100644 index c58b69a..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF +++ /dev/null @@ -1,26 +0,0 @@ -{ - "id": "7DF61ECF", - "path": "~/Documents/clinical_data_course/Clinical_Data_Management/00-intro.Rmd", - "project_path": "00-intro.Rmd", - "type": "r_markdown", - "hash": "0", - "contents": "", - "dirty": false, - "created": 1781803180451.0, - "source_on_save": false, - "relative_order": 1, - "properties": { - "source_window_id": "", - "Source": "Source", - "cursorPosition": "14,0", - "scrollLine": "0" - }, - "folds": "", - "lastKnownWriteTime": 1781029074, - "encoding": "UTF-8", - "collab_server": "", - "source_window": "", - "last_content_update": 1781029074, - "read_only": false, - "read_only_alternatives": [] -} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF-contents deleted file mode 100644 index aca5a2d..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/7DF61ECF-contents +++ /dev/null @@ -1,44 +0,0 @@ ---- -title: "Clinical Data Management" ---- - - -```{r, include = FALSE} -ottrpal::set_knitr_image_path() -``` - -# Introduction - -```{r, fig.align='center', echo = FALSE, fig.alt= "Clinical Data Management, Written and assembled by: Kate Isaac & Carrie Wright, Contributors: Christopher Battiston, David Hanauer, Guilherme Del Fiol, Harry Hochheiser, Jennifer Kelleher, Abby Robbertz, & Meghan McGrady", out.width= "100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.g34e03a999ff_0_165#slide=id.g34e03a999ff_0_165") -``` - - -## Motivation - - -This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines. - -This course is intended to provide an overview of the different kinds of data commonly used in clinical research, the questions that can typically be asked with such data, as well as guidance for how to manage this data. - - -```{r for_individuals_who, fig.align='center', echo = FALSE, fig.alt= "For individuals who: Have clinical data and don’t know what to do with it. Want a basic overview of what the clinical data represent and what questions can be asked.Need to know the basics of how to manage clinical data properly", out.width= "100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.g116525eff64_0_96#slide=id.g116525eff64_0_96") -``` - - -## Topics Covered - -```{r topics_covered, fig.align='center', echo = FALSE, fig.alt= "Concepts discussed in Clinical Data course: What does your clinical data type represent? How do you handle the clinical data securely and ethically? What types of analysis questions can one ask when it comes to different kinds of clinical data? Find resources, tools and tutorials to help you process and work with your data", out.width= "100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.g11db7c97851_0_143#slide=id.g11db7c97851_0_143") -``` - - -## Curriculum - -The course will cover key underlying principles and concepts in ethical data handling. - -```{r learning_objectives, fig.align='center', echo = FALSE, fig.alt= "Overall Course Learning Objectives. This course will demonstrate how to: List common sources and types of clinical data, Explain the unique aspects related to clinical data, Understand uses and the questions that can be asked with the major types of clinical data,Recognize major tools and methods for managing clinical data ethically and securely", out.width= "100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?slide=id.gd422c5de97_0_10#slide=id.gd422c5de97_0_10") -``` - diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/A364B9F6-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/A364B9F6-contents deleted file mode 100644 index dc9bf90..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/A364B9F6-contents +++ /dev/null @@ -1,29 +0,0 @@ ---- -title: "Untitled" -output: html_document -date: "2026-06-18" ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -## R Markdown - -This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . - -When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this: - -```{r cars} -summary(cars) -``` - -## Including Plots - -You can also embed plots, for example: - -```{r pressure, echo=FALSE} -plot(pressure) -``` - -Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot. diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36 b/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36 deleted file mode 100644 index 48e5340..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36 +++ /dev/null @@ -1,26 +0,0 @@ -{ - "id": "B3737D36", - "path": "~/Documents/clinical_data_course/Clinical_Data_Management/01c-how_to_collect_data.Rmd", - "project_path": "01c-how_to_collect_data.Rmd", - "type": "r_markdown", - "hash": "0", - "contents": "", - "dirty": false, - "created": 1781803494411.0, - "source_on_save": false, - "relative_order": 3, - "properties": { - "source_window_id": "", - "Source": "Source", - "cursorPosition": "6,850", - "scrollLine": "0" - }, - "folds": "", - "lastKnownWriteTime": 1781805287, - "encoding": "UTF-8", - "collab_server": "", - "source_window": "", - "last_content_update": 1781805287104, - "read_only": false, - "read_only_alternatives": [] -} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36-contents deleted file mode 100644 index ea0f654..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/B3737D36-contents +++ /dev/null @@ -1,144 +0,0 @@ -# How to Collect Clinical Data - -## Learning Objectives - -## Case Report Forms (CRFs) - -In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter -- specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). - -Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on **accessibility, sensitivity, specificity, and comfort for people** - -::: {.notice} - -While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren't necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions -- the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. - -::: - -Here, we explore the different categories of questions that may be included within case report forms: - -1. **Demographic and Socioeconomic Questions** - -Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. - -- **Sex and Gender**: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. -- **Ethnicity and Race**: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. -- **Socioeconomic Status**: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, "What is your current employment status?" with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. - -It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. - -2. **Health and Medial History Questions** - -Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. - -- **Medical Conditions and History**: Questions about past and present health conditions should use clear, accessible language. For example, "Have you ever been diagnosed with any of the following conditions? (Please check all that apply)" followed by a comprehensive list containing all necessary options. -- **Medication Use**: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. -- **Disability and Functional Status**: For many populations, it is important to use person-first language, such as "Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?" and provide space for participants to describe their specific needs. However, different populations have different preferences. The "Disability Language Style Guide" from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic [@ncdj_style_guide]. - -Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. - -3. **Experience and Quality of Life Questions** - -Understanding how health conditions and treatments affect participants' daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. - -- **Daily Living and Social Functioning**: Questions like "How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?" can help assess the impact on daily life, with options ranging from "never" to "always." -- **Emotional and Psychological Well-Being**: Including questions such as "In the past week, how often have you felt anxious or depressed?" using a scale from "not at all" to "very often" can provide insights into mental health needs. -- **Support Systems and Social Networks**: Asking about social support (e.g., "Do you have someone you can rely on for emotional support?") can help identify participants' needs for social and emotional resources. - -Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. - -4. **Sexual and Reproductive Health Questions** - -Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. - -- **Sexual Orientation and Gender Identity (SOGI)**: Instead of just predefined categories, open-ended questions like "How would you describe your sexual orientation?" and "What is your gender identity?" allow participants to self-identify. Offering the option to skip these questions respects participants' privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. -- **Reproductive Health**: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, "Are you currently using any form of contraception? If yes, please specify." -- **Sexual Activity and History**: Questions should be direct but framed sensitively, such as "Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs." - -These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. - -5. **Treatment Preferences and Decision-Making Questions** - -Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. - -- **Decision-Making Preferences**: Questions like "How involved would you like to be in decisions about your healthcare?" offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. -- **Cultural and Religious Considerations**: Asking, "Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?" ensures that care is respectful and culturally appropriate. -- **Treatment Burden**: Questions such as "What level of inconvenience or side effects would be acceptable to you when considering a treatment?" help to gauge participants' preferences and comfort levels. - -These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. - -6. **Accessibility and Accommodation Needs Questions** - -To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. - -- **Language and Communication Needs**: "What is your preferred language for communication? Do you need an interpreter or translated materials?" These questions help ensure that participants can understand the materials. -- **Physical Accessibility**: Asking, "Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?" ensures physical accessibility. -- **Format Preferences**: "Would you prefer to complete this form online, on paper, or verbally with assistance?" helps accommodate different needs and preferences. -Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. - -Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. - -7. **Cultural Sensitivity and Identity Questions** - -CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. - -- **Cultural Identity and Practices**: An open-ended question such as "Are there any cultural practices or beliefs that are important for us to be aware of in your care?" allows participants to share relevant information. -- **Dietary Restrictions and Preferences**: Asking, "Do you have any dietary restrictions or preferences that are culturally or religiously motivated?" ensures that these are respected. -- **Community and Belonging**: "Is there anything about your community or background that you would like us to know to provide better care?" encourages participants to share relevant aspects of their identity. - -These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. - -8. **Ensuring Comfort, Trust, and Privacy Questions** - -Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. - -- **Comfort and Confidentiality**: Asking, "Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?" helps build trust. -- **Feedback and Preferences**: "Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?" invites participants to share their feedback. -- **Consent and Voluntary Participation**: Questions like, "Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary." reinforce autonomy and respect. - -Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. - -::: {.warning} - -The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. - -::: - -Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. By thoughtfully addressing both unique study requirements and universally relevant data points, researchers can optimize CRFs for consistency across clinical studies. - -## Clinical Studies and Trials - -Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. - -Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. - -::: {.definition} -The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." -::: - -The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. - -::: {.definition} -**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. -::: - -## Research Design Considerations - -Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. - -One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, - -* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. -* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. -* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. -* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. - -Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: - -* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. -* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. -* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. -* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. - -With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. - -## Summary diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/C8C234F9-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/C8C234F9-contents deleted file mode 100644 index 4a308ea..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/C8C234F9-contents +++ /dev/null @@ -1,77 +0,0 @@ -# Specific Clinical Data Types - -## Learning Objectives - -```{r, fig.align='center', echo = FALSE, fig.alt= "Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data", out.width="100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g3385bea4ad0_0_30" ) -``` - - - -## Physiological - -## Monitoring data - -:::bluebox -This section was written by: Jennifer Kelleher, Ph.D.^1^; Abigail S. Robbertz, Ph.D.^1^; and Meghan E. McGrady, Ph.D.^1,2^ - -**NOTE:** Jennifer Kelleher, Ph.D.^1^ and Abigail S. Robbertz, Ph.D.^1^ contributed equally. - -^1^ Center for Adherence and Self-Management, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA - -^2^ Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA - -The work discussed in this section was also supported by the National Cancer Institute at the National Institutes of Health (R21CA263704, K07CA200668) to MEM. JK and ASR are supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development at the National Institutes of Health (T32HD068223). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. - -::: - -Electronic monitoring devices are digital tools that can be used to track health behaviors over time such as: - -* Sleep -* Physical activity -* Medication adherence -* Calorie intake - -Electronic monitoring devices can also be used to assess physical health indicators including: -* Blood glucose levels -* Blood pressure -* Heart rate and heart rate variability -* Oxygen saturation - -Electronic monitoring devices enable researchers to track day-to-day health behaviors in the patient’s **"real-world" setting**. This allows researchers to explore patterns or changes in a patient’s health behavior and provides a richer understanding of **daily behavior over time**. - - -### Benefits of Monitoring Data - -1) Electronic monitoring devices often include data transmission abilities that enable healthcare providers or researchers to access these data in near **real-time** potentially informing intervention and/or medical decision-making. - -1) Electronic monitoring devices also have the potential to produce **more accurate** estimates of health behaviors than alternative strategies (e.g., self-report) as they are not subject to recall bias and can detect efforts to inflate adherence due to social desirability. - -### Considerations - -:::warning -This section is not exhaustive. Research teams are strongly encouraged to consult with experts with experience and training in collecting and analyzing data from specific devices. - -To ensure the outcome variables are aligned with the research question of interest and [ethical and age/developmental considerations](https://pmc.ncbi.nlm.nih.gov/articles/PMC10798216/) (@psihogios_ethical_2024; @modi_pediatric_2012) have been appropriately accounted for, readers are encouraged to consult with researchers in their field who have integrated these measurement strategies into their work. -::: - -#### Medication Adherence - -There are three major components of medical adherence (the tracking of taking medication): - -- Initiation: Starting a prescribed regimen -- Implementation: The amount of which a patient's medication-taking behavior corresponds with the treatment regimen or protocol -- Discontinuation: Stopping a perscribed regimen - -For more information see: - -- [A new taxonomy for describing and defining adherence to medications (Vrijens et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC3403197/) -- [Pediatric self-management: A framework for research, practice, and policy. (Modi et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC9923567/) - -## Radiology - -## Pathology - -## Synthetic Data - -## Summary \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7 b/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7 deleted file mode 100644 index 993c88c..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7 +++ /dev/null @@ -1,26 +0,0 @@ -{ - "id": "DFF8DCC7", - "path": "~/Documents/clinical_data_course/Clinical_Data_Management/02-data_uses.Rmd", - "project_path": "02-data_uses.Rmd", - "type": "r_markdown", - "hash": "0", - "contents": "", - "dirty": false, - "created": 1781804963277.0, - "source_on_save": false, - "relative_order": 6, - "properties": { - "source_window_id": "", - "Source": "Source", - "cursorPosition": "295,135", - "scrollLine": "288" - }, - "folds": "", - "lastKnownWriteTime": 1781805267, - "encoding": "UTF-8", - "collab_server": "", - "source_window": "", - "last_content_update": 1781805267378, - "read_only": false, - "read_only_alternatives": [] -} \ No newline at end of file diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7-contents deleted file mode 100644 index d7dd267..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/DFF8DCC7-contents +++ /dev/null @@ -1,298 +0,0 @@ ---- -title: Clinical Data Uses ---- - -```{r, include = FALSE} -ottrpal::set_knitr_image_path() -``` - -# Clinical Data Uses - -## Learning Objectives - -## General uses of clinical data - -### Cancer Prevention and Care - -The near universal adoption of electronic health record (EHR) systems in the US has created unprecedented opportunities to improve cancer prevention and care. As described in previous chapters, EHR systems store comprehensive longitudinal records of a patient's interactions with a healthcare system, including data about: - - - demographics - - conditions - - family history - - clinical notes - - laboratory test results - - medications - - procedures - - imaging - - genetic test reports - - ```{r, fig.align='center', echo = FALSE, fig.alt= "Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. ", out.width="100%"} - ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g338f75828af_0_7" ) - ``` - -These data can be accessed not only by health professionals, but also by patients through patient portals. EHR data can also be used to enable data-driven interventions such as provider- and patient-facing clinical decision support (CDS) and population health management (PHM). - -CDS has been defined as tools that "provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care" [@Osheroff_2007]. Examples of widely adopted CDS tools with demonstrated effectiveness for cancer prevention, diagnosis, and care include: - - * provider and patient reminders for cancer screening - * decision aids and shared decision-making tools (e.g., for prostate cancer treatment, lung cancer screening) - * AI-supported diagnostic imaging - * chemotherapy decision support - * at-home symptom care - -While CDS tools generally provide decision support focused on a specific patient at a time, PHM are strategies that target specific patient populations [@Swarthout_Bishop_2017]. PHM efforts generally consist of: - - * population algorithms that are applied over EHR and other data sources to identify individuals who are eligible for a specific healthcare intervention (e.g., colorectal cancer screening, tobacco cessation, HPV vaccination) - * patient engagement strategies (e.g., patient portals, text messaging, chatbots, patient navigators) that provide education and access to those interventions - * analytic tools that assess the effectiveness of the PHM program. - -Several PHM programs have demonstrated to be effective in increasing the uptake of cancer prevention. For example, the colorectal cancer screening program at Kaiser Permanente uses digital (i.e., text messaging, patient portal), mailed, and patient navigation approaches to increase colorectal cancer screening by mailing Fecal Immunohistochemical Test (FIT) kits to patients' homes [@Gupta2020]. Also the Cancer Moonshot BRIDGE trial used the GARDE platform [@Bradshaw2022] (ITCR-funded) to identify candidates for genetic testing of hereditary cancer syndromes based on EHR data; and for patient outreach, pre- and post-test education via automated chatbots [@Kaphingst2024]. - - -```{r, fig.align='center', echo = FALSE, fig.alt= "Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM.", out.width="100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g338f75828af_0_28" ) -``` - -Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. - -While some CDS and PHM approaches have been successfully adopted widely, emerging technologies such as the use of generative AI approaches to analyze diagnostic imaging, large language models (LLMs) to extract information from narrative texts (e.g., clinical notes), LLM-based chatbots to communicate with patients, and digital health tools such as home-based sensors are creating unprecedented opportunities for next generation CDS and PHM. These approaches have the potential to enable significant breakthroughs through the implementation of patient-tailored cancer prevention and care at a population scale. Nevertheless, substantial research is needed to ensure effective and fair implementation of these CDS and PHM interventions. - - - -## Types of questions that can be asked with clinical data - -### Risk Prediction - -Risk prediction in clinical research involves using data to assess the likelihood of certain outcomes or events occurring in patients. This could include predicting the risk of developing a particular disease, experiencing a specific complication, or responding to a treatment. - -Data used for risk prediction can come from various sources, including: - - * Clinical Data: This includes patient demographics, medical history, laboratory results, and imaging studies. - * Genetic Data: Genetic information, such as DNA sequencing results, can provide valuable insights into an individual's susceptibility to certain diseases. - * Environmental and Lifestyle Data: Factors such as diet, exercise habits, smoking status, and environmental exposures can influence disease risk and may be included in risk prediction models. - * Biomarkers: Biological markers indicative of disease or physiological processes can be used as predictors in risk models [@Bodaghi_Fattahi_Ramazani_2023]. - -Once relevant data is collected, statistical and machine learning techniques can be applied to develop predictive models. These models aim to identify patterns and relationships within the data that are associated with the outcome of interest. Common techniques include logistic regression, decision trees, random forests, support vector machines, and neural networks. - -After the model is trained on a dataset, it can be validated using independent datasets to assess its performance and generalizability. Once validated, the model can be used to predict risk in new patients based on their individual characteristics and data. - -Clinical prediction rules are a subset of risk prediction models, specific to clinical research. - -Examples of risk prediction models ... - -Risk prediction models are important because ... - -Overall, risk prediction in clinical research allows healthcare professionals to identify individuals at higher risk of certain outcomes, enabling targeted interventions, personalized treatments, and more efficient resource allocation. - -### Cohort identification for research - -Clinical data plays a crucial role in cohort identification for research purposes. Researchers typically use electronic health records (EHRs), medical databases, or registries to identify cohorts based on specific criteria such as age, gender, medical conditions, treatments, medications, and outcomes. Advanced data mining and natural language processing techniques can also be employed to extract relevant information from unstructured data sources like clinical notes. Once cohorts are identified, researchers can analyze the data to study disease progression, treatment effectiveness, and outcomes. - -Cohort identification is important regardless of research study type, but to provide specific examples: - -* Research Design: Identifying cohorts allows researchers to design studies with appropriate inclusion or exclusion criteria. By selecting specific groups of individuals with similar characteristics or exposures, researchers can investigate hypotheses effectively. -* Clinical Insights: Cohort studies enable researchers to observe the natural history of diseases, track outcomes over time, and assess the effectiveness of interventions or treatments. Understanding how different factors influence disease progression or treatment response can inform clinical decision-making and improve patient care. -* Epidemiological Studies: Cohort identification is crucial for epidemiological research to understand the incidence, prevalence, and risk factors associated with diseases. By following cohorts over time, researchers can identify trends, patterns, and associations that contribute to our understanding of disease causation and prevention. -* Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. -* Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. - - -### Clinical studies and trials - -Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. - -Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. - -::: {.definition} -The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." -::: - -The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. - -::: {.definition} -**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. -::: - -Here, we explore the different categories of questions that can be addressed using clinical trials data, ranging from the evaluation of treatment efficacy and safety to the exploration of predictive factors for disease prognosis and the personalization of medical care. - -1. **Efficacy and Effectiveness Questions** - -One of the primary objectives of clinical trials is to determine the **efficacy of new treatments or interventions**. Clinical data is used to answer fundamental questions such as "Is the new treatment effective?" and "How does its effectiveness compare to existing treatments?" - -Researchers design studies to measure the **magnitude of treatment effects**, comparing outcomes between treatment and control groups (e.g., a new drug versus a placebo or standard care). - -Beyond controlled settings, data from real-world evidence (RWE) studies can further address questions about the effectiveness of treatments when applied in routine clinical practice. For example, "Is Drug A more effective than Drug B in managing hypertension in a real-world patient population?" Such questions help to assess how treatments perform outside the controlled environment of clinical trials where patient adherence comorbidities, and polypharmacy may influence outcomes. While clinical trials are an important source of real-world data (RWD), they are typically conducted under tightly controlled conditions, which may not fully reflect the complexities of real-world studies. This makes RWE particularly valuable for understanding how treatments work when patients have varying levels of adherence or multiple chronic conditions that are often excluded from clinical trails. - -::: {.definition} -**Comorbidities**: refer to the presence of one or more additional medical conditions or diseases that coexist with a primary condition in a patient. These conditions can occur independently or be related to the primary disease. -::: - -2. **Safety and Tolerability Questions** - -Safety is a critical aspect of any clinical study, and clinical data is essential in identifying and characterizing the safety profile of new treatments. Questions like "What are the common adverse effects of the new treatment?" or "What is the incidence of serious adverse events (SAEs) in patients receiving Drug A?" are addressed by carefully monitoring and recording adverse events throughout the study. Comparative safety questions also arise, such as "Does Drug A have fewer adverse effects compared to Drug B?" These questions are crucial in weighing the benefits of a treatment against its risk and in identifying specific populations that may be at higher risk of adverse reactions. - -::: {.definition} -**Serious Adverse Events (SAEs)**: Any medical occurrence during a clinical trial or medical treatment that results in significant negative outcomes, such as death, life-threatening situations, hospitalization (or its prolongation), persistent or significant disability, or congenital anomalies. SAEs also include any event that requires intervention to prevent one of these outcomes. -::: - -::: {.definition} -**Incidence**: Refers to the rate or number of new cases of SAEs that occur within a specific population during a defined time period, typically during a clinical trial or study. Is it usually expressed as a proportion, such as the number of SAEs per 100 or 1,000 participants, and helps measure how frequently these serious events happen among those exposed to a particular treatment or intervention. -::: - -3. **Comparative Effectiveness and Cost-Effectiveness Questions** - -Beyond safety and efficacy, clinical data can be used to evaluate the comparative effectiveness of different treatments and their cost-effectiveness. These questions often guide healthcare policy and clinical guidelines: "Is Drug A more cost-effective than standard care for managing chronic heart failure?" and "What is the cost per quality-adjusted life year (QALY) gained with the new intervention?" Such analyses provide valuable information for stakeholders, including healthcare providers, payers, and policymakers, to make informed decisions on resource allocation. - -::: {.definition} -**Cost per quality-adjusted life year (QALY)** is a metric used in health economics to assess the value of a medical intervention by measuring the cost of gaining one year of life adjusted for its quality. A QALY incorporates both the **quantity** and **quality** of life lived, where one QALY is equivalent to one year of life in perfect health. If a treatment improves both the length and quality of life, it earns more QALYs. The **cost per QALY** is calculated by dividing the cost of the treatment by the number of QALYs gained, helping to determine the cost-effectiveness of healthcare interventions. Lower cost per QALY indicates better value for money in terms of health benefits achieved [-@Drummond_Sculpher_Claxton_Stoddart_Torrance_2015]. -::: - -4. **Mechanistic and Biomarker Questions** - -Clinical studies often explore not just whether a treatment works, but also how it works. Mechanistic questions delve into the underlying biological pathways affected by a treatment. For instance, "Does Drug A work by inhibiting a specific enzyme involved in the disease process?" Biomarker data can play a key role in such analyses, helping to identify molecular markers that predict response to treatment. Questions like "Are there biomarkers that can predict which patients are more likely to benefit from Drug A?" help in developing targeted therapies and personalized treatment approaches. - -5. **Predictive and Prognostic Questions** - -Clinical trials and studies frequently aim to identify factors that predict disease outcomes or treatment responses. For example, "What baseline characteristics predict a better response to Drug A?" or "What are the predictors of mortality in patients with severe heart failure?" Such prognostic and predictive questions are crucial for identifying high-risk patients, guiding treatment decisions, and developing clinical guidelines. Identifying patient subgroups that benefit more or less from a treatment also facilitates personalized medicine approaches, ensuring that the right treatment is delivered to the right patient at the right time. - -With the advent of personalized medicine, clinical data is increasingly being used to answer questions tailored to individual patient profiles. For instance, "What patient characteristics modify the effect of the treatment?" or "Can we predict which patients are most likely to benefit from Drug A using machine learning models?" These questions are at the forefront of precision medicine, where the goal is to customize healthcare to each patient based on their unique genetic, biomarker, and clinical characteristics. - -6. **Quality of Life and Patient-Reported Outcome Questions** - -In modern clinical research, the impact of treatments on patients' quality of life and their subjective experiences has gained prominence. Questions such as "How does Drug A affect patients' quality of life compared to standard care?" or "What are the patient-reported outcomes associated with the new intervention?" are increasingly addressed in clinical trials. Data from quality of life assessments which are standardized and validated questionnaires (for example, the Health Assessment Questionnaire or HAQ), patient-reported outcome measures (PROMs, tools or questionnaires used to assess a patient's health status or health-related quality of life directly from the patient's perspective), and other patient-centric endpoints (Smoking History, Surgical History, etc.) provide valuable insights into how treatments affect patients beyond traditional clinical endpoints. - -7. **Longitudinal and Follow-Up Questions** - -Long-term follow-up studies are essential for understanding the durability of treatment effects and any delayed adverse effects. Questions like "What are the long-term outcomes associated with Drug A?" and "Is there a sustained benefit of Drug A in reducing symptoms after five years?" are vital for assessing the overall value of treatments. Such longitudinal analyses help in determining the optimal duration of therapy, the need for maintenance treatments, and the overall benefit-risk profile of an intervention. - -8. **Safety in Special Populations Questions** - -Clinical data can also be used to evaluate the safety and efficacy of treatments in special populations, such as children, pregnant women, or elderly patients. Questions such as "Is Drug A safe for use in pregnant women?" and "What is the risk of adverse events when Drug A is administered to patients with multiple comorbidities?" are essential for developing age- and condition-specific clinical guidelines. These questions help ensure that treatments are tailored to the needs of different patient populations, minimizing harm and maximizing benefits. - -9. **Adherence and Compliance Questions** - -Patient adherence to prescribed treatment regimens can significantly impact the outcomes of clinical studies. Questions like "What factors influence patient adherence to the new treatment?" and "How does adherence affect treatment efficacy?" are addressed through analyzing adherence data through monitors if a regulated study, or with interim/baseline analysis if non-regulated. Understanding these factors can lead to interventions that improve adherence and, consequently, the effectiveness of the treatment. - -10. **Real-World Evidence and Generalizability Questions** - -Real-world evidence (RWE) studies help bridge the gap between clinical trial results and clinical practice. Questions such as "How generalizable are the results of this clinical trial to the broader population?" or "What is the impact of real-world use patterns on treatment outcomes?" are essential for understanding how well clinical trial findings apply to everyday clinical settings. These questions help determine if the benefits observed in clinical trials can be replicated in diverse patient populations under routine care conditions. - -11. **Prevention and Risk Reduction Questions** - -Clinical data is also crucial in answering questions related to prevention and risk reduction strategies. For example, "Can the new treatment reduce the risk of developing diabetes in high-risk individuals?" or "What factors are associated with a reduced risk of cardiovascular disease in a large cohort study?" These questions are fundamental in preventive medicine, guiding public health interventions and informing clinical practice. - - -Clinical Data provides a wealth of information that can be leveraged to answer a broad array of questions in clinical trials and studies. From understanding treatment efficacy and safety to exploring long-term outcomes, quality of life impacts, and the potential for personalized medicine, clinical data is foundational to advancing medical knowledge and improving patient care. Case report forms (CRFs) are instrumental in identifying patient cohorts or subgroups, documenting baseline characteristics, and capturing information on comorbidities, while also monitoring adherence data, which are essential for ensuring accurate and meaningful analysis. The type of questions that can be asked and answered are continually evolving as new data sources, analytical methods, and research paradigms emerge, further enriching the field of clinical research. - - -### Retrospective analysis - -Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. - -Here, we explore the different categories of questions that can be addressed using retrospective data analysis: - -1. **Descriptive Questions** - -Descriptive questions aim to summarize and describe the characteristics of a patient population, disease, or healthcare process. They provide a foundational understanding of a dataset and are often the starting point for more complex analyses. - -- **Patient Demographics and Characteristics**: What are the demographic profiles (age, gender, ethnicity, socioeconomic status) of patients diagnosed with a specific condition? What are the common comorbidities and risk factors in this population? -- **Disease Prevalence and Incidence**: What is the prevalence or incidence of a specific disease or condition in a particular population or geographic area over a defined period? -- **Clinical Presentation**: What are the most common presenting symptoms, clinical signs, or laboratory findings associated with a specific disease or condition? - -These questions help in understanding the baseline characteristics and epidemiology of diseases, which is crucial for planning further studies or developing public health interventions. - -2. **Treatment and Intervention Questions** - -Retrospective data can be used to examine the real-world effectiveness, safety, and patterns of treatment and interventions. These questions are critical for understanding how treatments are applied in clinical practice and their outcomes: - -- **Treatment Effectiveness**: What is the effectiveness of a specific treatment or intervention in reducing symptoms, improving quality of life, or achieving clinical outcomes in a real-world setting? -- **Adherence and Persistence**: What are the rates of adherence and persistence with a prescribed treatment regimen over time? What factors are associated with higher or lower adherence rates? -- **Comparative Effectiveness**: How does the effectiveness of one treatment compare to another in a similar population? What are the relative benefits and risks associated with different treatment options? - -By examining historical data, researchers can gain insights into how different treatments perform outside the controlled environment of clinical trials, thereby informing clinical decision-making and guidelines. - -3. **Outcome and Prognostic Questions** - -Understanding patient outcomes and prognostic factors is central to retrospective analyses. These questions focus on the end results of healthcare practices and patient management, including survival, complications, and quality of life: - -- **Survival and Mortality**: What are the survival rates and mortality rates associated with a particular disease, condition, or treatment? What factors are associated with increased or decreased survival rates? -- **Complication Rates**: What are the rates and types of complications associated with specific diseases, surgeries, or treatments? Are there identifiable risk factors for these complications? -- **Prognostic Factors**: What are the key prognostic factors (e.g., age, stage of disease, comorbidities) that influence the outcomes of patients with a particular condition? - -These questions help identify factors that influence patient outcomes, guiding clinicians in predicting disease progression and tailoring individual treatment plans. - -4. Risk Factor and Predictive Modeling Questions - -Retrospective analyses are often used to identify potential risk factors for diseases and develop predictive models. These questions aim to uncover associations between variables and outcomes to help predict future events: - -- **Risk Factors for Disease**: What are the risk factors associated with developing a particular disease or condition? Are there demographic, genetic, behavioral, or environmental factors that significantly increase the risk? -- **Prediction Models**: Can we develop a predictive model to estimate the likelihood of disease progression, relapse, or adverse outcomes based on historical patient data? -- **Multivariable Analysis**: How do multiple factors interact to influence the risk of an outcome? Are there synergistic or antagonistic effects between different risk factors? - -These questions are critical for developing tools that help clinicians assess risk and make more informed decisions in preventive care and early intervention. - -5. **Healthcare Utilization and Cost Questions** - -Understanding how healthcare resources are utilized and the associated costs is important for optimizing healthcare delivery. Retrospective data can provide valuable insights into patterns of care and resource allocation for questions such as: - -- **Healthcare Resource Utilization**: What are the patterns of healthcare utilization (e.g., hospitalizations, emergency room visits, outpatient visits) for patients with a specific condition? How does utilization vary by patient demographics, disease severity, or geographic location? -- **Cost of Care**: What are the direct and indirect costs associated with managing a specific disease or condition? How do different treatment options impact overall healthcare costs? -- **Cost-Effectiveness**: How does the cost-effectiveness of one treatment or intervention compare to another? What are the economic impacts of different clinical management strategies? - -These questions can inform healthcare policy, budget planning, and the development of strategies to improve cost-effectiveness and access to care. - -6. **Quality of Care and Patient Safety Questions** - -Retrospective analysis can be used to evaluate the quality and safety of care provided to patients, identifying areas for improvement and potential patient safety concerns, helping to answer questions such as: - -- **Quality Indicators**: Quality indicators are standardized measures of health care quality and may focus on more specialized groups of care like preventative, inpatient or emergency care, pediatric or maternal health, etc. What are the levels of observed mortality or morbidity for a certain population in a specific setting? How utilized are certain procedures? [@AHRQ] -- **Adverse Events and Safety**: What is the incidence of adverse events, medication errors, or healthcare-associated infections in a particular patient population? What factors are associated with higher or lower rates of adverse events? -- **Patient Satisfaction and Experience**: What are the reported levels of patient satisfaction with healthcare services? How does satisfaction vary by demographic or clinical characteristics? - -These questions help in assessing healthcare quality, identifying gaps in care, and developing interventions to enhance patient safety and satisfaction. - -7. **Temporal and Trend Analysis Questions** - -Retrospective studies often involve analyzing temporal patterns to understand how diseases, treatments, or outcomes change over time. These questions help in recognizing trends and patterns that can inform future research and policy decisions: - -- **Temporal Trends in Disease Incidence and Prevalence**: How have the incidence and prevalence of a specific disease changed over the past decade? Are there seasonal or cyclical patterns in disease occurrence? -- **Trends in Treatment Practices**: How have treatment practices and prescribing patterns evolved over time? Are there emerging trends in the use of certain medications or interventions? -- **Trends in Outcomes and Survival**: How have patient outcomes, such as survival rates or complication rates, changed over time with advances in treatment and healthcare delivery? - -These questions are essential for monitoring public health trends, evaluating the impact of interventions, and planning future healthcare strategies. - -8. **Subgroup and Stratified Analysis Questions** - -Subgroup analyses can help uncover differences in disease characteristics, treatment responses, and outcomes among different patient groups. This type of analysis is particularly valuable for personalized medicine and addressing questions related to health disparities: - -- **Differences by Demographic Group**: How do treatment outcomes, risk factors, or disease characteristics differ between various demographic groups (e.g., age, gender, race/ethnicity)? -- **Subgroup-Specific Effectiveness**: Does a particular treatment work better in one subgroup of patients compared to another? What factors might explain these differences? -- **Health Disparities**: Are there disparities in healthcare access, treatment outcomes, or disease burden across different socioeconomic or minority groups? - -Questions focusing on subgroups are crucial for understanding variations in care and outcomes, identifying at-risk populations, and developing targeted interventions. - - -Retrospective analysis of clinical data allows researchers to ask a wide range of questions that provide valuable insights into patient characteristics, disease epidemiology, treatment effectiveness, outcomes, risk factors, healthcare utilization, and more. By carefully formulating questions and analyzing historical data, researchers can uncover patterns, identify trends, and generate evidence that informs clinical practice, healthcare policy, and future research directions. This chapter outlines the various categories of questions that can be addressed through retrospective analysis, highlighting the potential of this approach to advance knowledge and improve patient care in diverse clinical settings. - - -## Research Design Considerations - -Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. - -One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, - -* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. -* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. -* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. -* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. - -Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: - -* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. -* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. -* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. -* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. - -With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. - -## Conclusion diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/E2086159-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/E2086159-contents deleted file mode 100644 index dc638be..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/E2086159-contents +++ /dev/null @@ -1,10 +0,0 @@ -# ignore nonsense files -.DS_Store -.Rhistory -.local -.rstudio -.bash_history -spell_check_results.tsv -.RData -.httr-oauth -docker/git_token.txt diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/F5F672FD-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/F5F672FD-contents deleted file mode 100644 index 87b4a01..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/F5F672FD-contents +++ /dev/null @@ -1,17 +0,0 @@ -bookdown::gitbook: - css: assets/style_ITN.css - includes: - before_body: assets/big-image-itcr.html - after_body: assets/footer.html - highlight: tango - config: - toc: - collapse: section - before: | - - after: | -

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diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/FB2EFA6D-contents b/.Rproj.user/6F8C099B/sources/session-87f78f51/FB2EFA6D-contents deleted file mode 100644 index be8d1be..0000000 --- a/.Rproj.user/6F8C099B/sources/session-87f78f51/FB2EFA6D-contents +++ /dev/null @@ -1,23 +0,0 @@ ---- -title: "Informatics Technology for Cancer Research (ITCR): Clinical Data Management " -date: "`r format(Sys.time(), '%B, %Y')`" -site: bookdown::bookdown_site -documentclass: book -bibliography: [book.bib, packages.bib] -biblio-style: apalike -link-citations: yes -description: "Description about Course/Book." -favicon: assets/ITN_favicon.ico ---- - - -```{r include=FALSE} -# automatically create a bib database for R packages -knitr::write_bib(c( - .packages(), "bookdown", "knitr", "rmarkdown" -), "packages.bib") -``` - -# About this Course {-} - -This course is part of a series of courses created for the [Informatics Technology for Cancer Research (ITCR)](https://www.cancer.gov/about-nci/organization/cssi/research/itcr) Training Network (ITN). The ITN is a collaborative effort of researchers around the United States that supports cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: [National Cancer Institute (NCI)](https://www.cancer.gov/) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our [website](https://www.itcrtraining.org) at itcrtraining.org for more information. Except where otherwise indicated, the contents of this course are available for use under the Creative Commons Attribution 4.0 license. You are free to adapt and share the work, but you must give appropriate credit, provide a link to the license, and indicate if changes were made. Sample attribution: Cancer Data Management by Fred Hutchinson Data Science Lab (CC-BY 4.0). You can download the illustrations by clicking on this [link for our slides](https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?usp=sharing). diff --git a/.Rproj.user/6F8C099B/sources/session-87f78f51/lock_file b/.Rproj.user/6F8C099B/sources/session-87f78f51/lock_file deleted file mode 100644 index e69de29..0000000 diff --git a/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/6F8C099B87f78f51/chunks.json b/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/6F8C099B87f78f51/chunks.json deleted file mode 100644 index 92e21b0..0000000 --- a/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/6F8C099B87f78f51/chunks.json +++ /dev/null @@ -1 +0,0 @@ -{"chunk_definitions":[],"doc_write_time":1781803473} \ No newline at end of file diff --git a/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/s/chunks.json b/.Rproj.user/shared/notebooks/0B1EC249-01c-how_to_collect_data/1/s/chunks.json deleted file 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b/01c-how_to_collect_data.Rmd index ea0f654..debd86f 100644 --- a/01c-how_to_collect_data.Rmd +++ b/01c-how_to_collect_data.Rmd @@ -109,6 +109,8 @@ Designing specific and sensitive CRFs for clinical studies requires a thoughtful Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. +## Clinical Trials + Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. ::: {.definition} @@ -121,6 +123,22 @@ The NIH provides 4 questions and additional clarifications to consider when iden **Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. ::: +### Observational Studies + +In observational studies, patients are monitored and outcomes are measured, but there is no intervention or assignment ((Overview of clinical study designs)[https://pmc.ncbi.nlm.nih.gov/articles/PMC11009715/]). + +#### Case-control Studies + +In case-control studies, a group of individuals who have a certain disease or condition are identified as cases, and are compared to a group of individuals without the disease or condition (these are the controls). Group level summaries of outcomes, such as medical events or other biological measurements, are compared between the case and control groups. In case-control studies, there is a risk of bias, in which the group of cases and group of controls are systematically different, beyond the disease or condition that is being studied. Therefore, it is important to match the groups in terms of individual characteristics as much as possible. + +#### Cohort Studies + +Cohort studies follow study participants over time. Participants are recruited into the cohort if they share a specific characteristic that is being studied. They are then followed over time, and monitored to see which participants develop a specific outcome. Cohort studies can be prospective (the cohort is identified and then monitored), or retrospective (the cohort is identified about a time period has passed and past data is obtained). + +#### Cross-sectional Studies + +In a cross-sectional study, data is collected at a specific point in time. Cross-sectional studies are often used to ask questions about the prevalence of an exposure or an outcome (typically a disease or condition) in a population at a specific time point. + ## Research Design Considerations Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. diff --git a/02-data_uses.Rmd b/02-data_uses.Rmd index d7dd267..4279402 100644 --- a/02-data_uses.Rmd +++ b/02-data_uses.Rmd @@ -10,9 +10,7 @@ ottrpal::set_knitr_image_path() ## Learning Objectives -## General uses of clinical data - -### Cancer Prevention and Care +## Clinical Data Uses for Cancer Prevention and Care The near universal adoption of electronic health record (EHR) systems in the US has created unprecedented opportunities to improve cancer prevention and care. As described in previous chapters, EHR systems store comprehensive longitudinal records of a patient's interactions with a healthcare system, including data about: @@ -32,6 +30,14 @@ The near universal adoption of electronic health record (EHR) systems in the US These data can be accessed not only by health professionals, but also by patients through patient portals. EHR data can also be used to enable data-driven interventions such as provider- and patient-facing clinical decision support (CDS) and population health management (PHM). +```{r, fig.align='center', echo = FALSE, fig.alt= "Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM.", out.width="100%"} +ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g338f75828af_0_28" ) +``` + +Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. + +### Clinical Decision Support (CDS) + CDS has been defined as tools that "provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care" [@Osheroff_2007]. Examples of widely adopted CDS tools with demonstrated effectiveness for cancer prevention, diagnosis, and care include: * provider and patient reminders for cancer screening @@ -40,6 +46,8 @@ CDS has been defined as tools that "provide clinicians, staff, patients, or othe * chemotherapy decision support * at-home symptom care +### Population Health Management (PHM) + While CDS tools generally provide decision support focused on a specific patient at a time, PHM are strategies that target specific patient populations [@Swarthout_Bishop_2017]. PHM efforts generally consist of: * population algorithms that are applied over EHR and other data sources to identify individuals who are eligible for a specific healthcare intervention (e.g., colorectal cancer screening, tobacco cessation, HPV vaccination) @@ -48,17 +56,10 @@ While CDS tools generally provide decision support focused on a specific patient Several PHM programs have demonstrated to be effective in increasing the uptake of cancer prevention. For example, the colorectal cancer screening program at Kaiser Permanente uses digital (i.e., text messaging, patient portal), mailed, and patient navigation approaches to increase colorectal cancer screening by mailing Fecal Immunohistochemical Test (FIT) kits to patients' homes [@Gupta2020]. Also the Cancer Moonshot BRIDGE trial used the GARDE platform [@Bradshaw2022] (ITCR-funded) to identify candidates for genetic testing of hereditary cancer syndromes based on EHR data; and for patient outreach, pre- and post-test education via automated chatbots [@Kaphingst2024]. - -```{r, fig.align='center', echo = FALSE, fig.alt= "Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM.", out.width="100%"} -ottrpal::include_slide("https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit#slide=id.g338f75828af_0_28" ) -``` - -Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. +### Emerging Technology While some CDS and PHM approaches have been successfully adopted widely, emerging technologies such as the use of generative AI approaches to analyze diagnostic imaging, large language models (LLMs) to extract information from narrative texts (e.g., clinical notes), LLM-based chatbots to communicate with patients, and digital health tools such as home-based sensors are creating unprecedented opportunities for next generation CDS and PHM. These approaches have the potential to enable significant breakthroughs through the implementation of patient-tailored cancer prevention and care at a population scale. Nevertheless, substantial research is needed to ensure effective and fair implementation of these CDS and PHM interventions. - - ## Types of questions that can be asked with clinical data ### Risk Prediction @@ -96,22 +97,7 @@ Cohort identification is important regardless of research study type, but to pro * Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. * Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. - -### Clinical studies and trials - -Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. - -Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. - -::: {.definition} -The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." -::: - -The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. - -::: {.definition} -**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. -::: +### Questions that can be asked using clinical studies and trials Here, we explore the different categories of questions that can be addressed using clinical trials data, ranging from the evaluation of treatment efficacy and safety to the exploration of predictive factors for disease prognosis and the personalization of medical care. @@ -181,13 +167,13 @@ Real-world evidence (RWE) studies help bridge the gap between clinical trial res Clinical data is also crucial in answering questions related to prevention and risk reduction strategies. For example, "Can the new treatment reduce the risk of developing diabetes in high-risk individuals?" or "What factors are associated with a reduced risk of cardiovascular disease in a large cohort study?" These questions are fundamental in preventive medicine, guiding public health interventions and informing clinical practice. - Clinical Data provides a wealth of information that can be leveraged to answer a broad array of questions in clinical trials and studies. From understanding treatment efficacy and safety to exploring long-term outcomes, quality of life impacts, and the potential for personalized medicine, clinical data is foundational to advancing medical knowledge and improving patient care. Case report forms (CRFs) are instrumental in identifying patient cohorts or subgroups, documenting baseline characteristics, and capturing information on comorbidities, while also monitoring adherence data, which are essential for ensuring accurate and meaningful analysis. The type of questions that can be asked and answered are continually evolving as new data sources, analytical methods, and research paradigms emerge, further enriching the field of clinical research. +### Questions that can be asked with retrospective analysis -### Retrospective analysis +**Note**: I wonder if these are more accurately questions that can be asked with observational data analysis (either prospective or retrospective studies) -Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. +The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. Here, we explore the different categories of questions that can be addressed using retrospective data analysis: @@ -274,25 +260,4 @@ Questions focusing on subgroups are crucial for understanding variations in care Retrospective analysis of clinical data allows researchers to ask a wide range of questions that provide valuable insights into patient characteristics, disease epidemiology, treatment effectiveness, outcomes, risk factors, healthcare utilization, and more. By carefully formulating questions and analyzing historical data, researchers can uncover patterns, identify trends, and generate evidence that informs clinical practice, healthcare policy, and future research directions. This chapter outlines the various categories of questions that can be addressed through retrospective analysis, highlighting the potential of this approach to advance knowledge and improve patient care in diverse clinical settings. - -## Research Design Considerations - -Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. - -One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, - -* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. -* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. -* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. -* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. - -Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: - -* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. -* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. -* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. -* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. - -With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. - ## Conclusion diff --git a/Course_Name.rds b/Course_Name.rds index 2e8e4f66c8a92a8a150dc013bebfcc9785b4b1fd..5c6cced9de4e5fdebddd36252be57bb2b46ad6c7 100644 GIT binary patch literal 391 zcmV;20eJo&iwFP!000001D#Y)Pr@)1FM}oE1gAt}G=7Acp&qbmL|xi8O;!97CGSWP@N`fBW;9vY~z z&jo|kb;J{ZwduoI*JMW;t z(P6E{PQ%+06%j$+AOa!VOP>D)TkP^KAV~s(@zs;fE;U$dvHrs>URrF-EAm^@52rAP z6a=-YmtTSQ2^kH_6(rg=CU4W}$`(0Q%4Ohi+nuV|vDr+jh09&f-biVFmPShRv#C;i l<62kovxXa}qx5T1#Rp|YtH4OBK^|3y_X`!PPr7Ob004n1xMctU literal 323 zcmV-J0lfYniwFP!000001C3HmPs1<__4*-E%0>}_1N?@f3^;J*g2c4~iCdMmE{dR8 z)n?N8^DAk}nxq=la!Ksx=V$rl;}if~@O%ecUp~Qqe|w3a0Gvx11oDpLJp-?M!wKU# zZtuz*z;ldz@Wq)(OB7TF9TCO_1^dP(%~>r2vty__rV1W$=8OxhOUxM+9jJKS*^Qg2T2;t%wtZEw?bGvWy%EQ+BUJTMyRh2N6D+Vsp8d^# zlt!dUQ$p4k@Hs69>x2DY>OWy3mHWim;J?mY*mh~)_CKF=Nn{zM^VbKQF - - -## Motivation - - -This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines. - -This course is intended to provide an overview of the different kinds of data commonly used in clinical research, the questions that can typically be asked with such data, as well as guidance for how to manage this data. - - -For individuals who: Have clinical data and don’t know what to do with it. Want a basic overview of what the clinical data represent and what questions can be asked.Need to know the basics of how to manage clinical data properly - - -## Topics Covered - -Concepts discussed in Clinical Data course: What does your clinical data type represent? How do you handle the clinical data securely and ethically? What types of analysis questions can one ask when it comes to different kinds of clinical data? Find resources, tools and tutorials to help you process and work with your data - - -## Curriculum - -The course will cover key underlying principles and concepts in ethical data handling. - -Overall Course Learning Objectives. 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It may come in many forms and its sensitive nature requires careful management by researchers. - -## Learning Objectives - -Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data - - - -## Clinical data is unique - -Clinical data refers to information __collected from patients__ during healthcare delivery, clinical trials, and medical research. Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data with predictable and consistent formats, clinical data is often heterogeneous, integrating many forms of both structured and unstructured data: quantitative measurements, categorical data, genotyping, images, subjective narratives from patient notes, and objective observations or conclusions. The unstructured nature of free text from notes reflecting subjective patient experiences or qualitative insights from healthcare professionals especially adds a further layer of complexity. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data's unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare. Further, because clinical data contains sensitive, personal information about patients, there are additional security and ethics concerns in the handling and management of clinical data. - -## Major clinical data types - -Clinical data can come in many different forms, including - -* patient demographics -* medical history or records such as diagnoses, lab results, vital signs, medication records, or procedure history -* genetic reports -* health monitor data -* images -* scanned documents, and notes written by physicians, nurses, and other clinicians -* survey/ case report form (CRF responses) -* and more ... - -Clinical data refers to information collected from patients during healthcare delivery, clinical trials, and medical research and could include demographics, medical history, lab results, imaging, treatment outcomes, and more - -Some sources of clinical data are more prevalent and readily obtainable than others. For instance, notes, demographics, images, and histories or observations/records (lab results, vitals, medications, procedures) are often stored directly in electronic medical record systems making them more easily accessible and so are the focus of most Electronic Health Record (EHR) data research efforts. - -### Structured data - -Observational records such as test results and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Structured data types frequently used in EHR research consist of comprehensive longitudinal records of a patient's interactions with a healthcare system and may include demographics, diagnoses, lab values, procedures, vitals, and medication records. - -Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. - -The structured nature of this data allows for it to be stored in tables which may be indexed by a patient or visit ID and often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. - -Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or "billing codes", provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. - -Example of structured data, a table that includes patient id numbers, billing codes, dates, blood pressure measurements, weight measurements, height measurements, and prescribed medications coded using the National Drug Code (NDC) - -As we've described it, structured clinical data is expected to have similarities, although specific details may vary based on the type of EHR being used and any customizations to the EHR for the specific environment or institution in which data was collected (e.g., any specialized pre-processing by institutional research offices prior to providing data to researchers). So it is important for researchers to consider differences present in their data if they've obtained it from multiple contexts or institutions. - -### Unstructured data / clinical notes - -Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. - - -Unstructured Data - Data without specific format: includes images, pathology reports, radiology reports, clinical notes, discharge summaries and more. Includes an image of an x-ray and some clinical notes that states: Patient reports a sharp pain in the right lower abdomen for the past 24 hours.Temperature 98.6°F, BP 120/80, tenderness in the right lower quadrant. Suspected appendicitis. Refer to surgery for evaluation, initiate IV fluids, and pain management. - - -When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [@Dhir2008] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. - - -## How to acquire clinical data - -### Secondary Sources - - -## Description of data - -### File types - -### Metadata - - -## Summary diff --git a/docs/01-intro.md b/docs/01-intro.md deleted file mode 100644 index 0180bd7..0000000 --- a/docs/01-intro.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: "Course Title" ---- - - - - -# Introduction - - -## Motivation - - -## Target Audience - -The course is intended for ... - -## Curriculum - -The course covers... diff --git a/docs/01-intro_content.md b/docs/01-intro_content.md deleted file mode 100644 index 746b69a..0000000 --- a/docs/01-intro_content.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -title: Introduction ---- - - - -# Introduction - -## How clinical data differs from other data types - -Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data, clinical data is often heterogeneous, integrating quantitative measurements, categorical data, subjective narratives from patient notes, and objective observations from doctor notes and even possible image analysis. Free text adds a layer of complexity with unstructured information, reflecting subjective patient experiences or qualitative insights from healthcare professionals. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data's unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare. - -## Major clinical data types - -## Specific types of clinical data - -## How to acquire clinical data - -## Description of data - -### File types - -### Metadata - -## What questions can be asked with clinical data? diff --git a/docs/01b-specific_data_types.md b/docs/01b-specific_data_types.md deleted file mode 100644 index efb8955..0000000 --- a/docs/01b-specific_data_types.md +++ /dev/null @@ -1,75 +0,0 @@ -# Specific Clinical Data Types - -## Learning Objectives - -Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data - - - -## Physiological - -## Monitoring data - -:::bluebox -This section was written by: Jennifer Kelleher, Ph.D.^1^; Abigail S. Robbertz, Ph.D.^1^; and Meghan E. McGrady, Ph.D.^1,2^ - -**NOTE:** Jennifer Kelleher, Ph.D.^1^ and Abigail S. Robbertz, Ph.D.^1^ contributed equally. - -^1^ Center for Adherence and Self-Management, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA - -^2^ Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA - -The work discussed in this section was also supported by the National Cancer Institute at the National Institutes of Health (R21CA263704, K07CA200668) to MEM. JK and ASR are supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development at the National Institutes of Health (T32HD068223). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. - -::: - -Electronic monitoring devices are digital tools that can be used to track health behaviors over time such as: - -* Sleep -* Physical activity -* Medication adherence -* Calorie intake - -Electronic monitoring devices can also be used to assess physical health indicators including: -* Blood glucose levels -* Blood pressure -* Heart rate and heart rate variability -* Oxygen saturation - -Electronic monitoring devices enable researchers to track day-to-day health behaviors in the patient’s **"real-world" setting**. This allows researchers to explore patterns or changes in a patient’s health behavior and provides a richer understanding of **daily behavior over time**. - - -### Benefits of Monitoring Data - -1) Electronic monitoring devices often include data transmission abilities that enable healthcare providers or researchers to access these data in near **real-time** potentially informing intervention and/or medical decision-making. - -1) Electronic monitoring devices also have the potential to produce **more accurate** estimates of health behaviors than alternative strategies (e.g., self-report) as they are not subject to recall bias and can detect efforts to inflate adherence due to social desirability. - -### Considerations - -:::warning -This section is not exhaustive. Research teams are strongly encouraged to consult with experts with experience and training in collecting and analyzing data from specific devices. - -To ensure the outcome variables are aligned with the research question of interest and [ethical and age/developmental considerations](https://pmc.ncbi.nlm.nih.gov/articles/PMC10798216/) (@psihogios_ethical_2024; @modi_pediatric_2012) have been appropriately accounted for, readers are encouraged to consult with researchers in their field who have integrated these measurement strategies into their work. -::: - -#### Medication Adherence - -There are three major components of medical adherence (the tracking of taking medication): - -- Initiation: Starting a prescribed regimen -- Implementation: The amount of which a patient's medication-taking behavior corresponds with the treatment regimen or protocol -- Discontinuation: Stopping a perscribed regimen - -For more information see: - -- [A new taxonomy for describing and defining adherence to medications (Vrijens et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC3403197/) -- [Pediatric self-management: A framework for research, practice, and policy. (Modi et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC9923567/) - -## Radiology - -## Pathology - -## Synthetic Data - -## Summary diff --git a/docs/02-chapter_of_course.md b/docs/02-chapter_of_course.md deleted file mode 100644 index bfadb28..0000000 --- a/docs/02-chapter_of_course.md +++ /dev/null @@ -1,210 +0,0 @@ ---- -title: "Chapter of course" ---- - -*If you haven't yet read the getting started Wiki pages; [start there](https://github.com/jhudsl/DaSL_Course_Template_Bookdown/wiki/Getting-started) - -Every chapter needs to start out with this chunk of code: - - - -## Learning Objectives - -*Every chapter also needs Learning objectives that will look like this: - -This chapter will cover: - -- {You can use https://tips.uark.edu/using-blooms-taxonomy/ to define some learning objectives here} -- {Another learning objective} - -## Libraries - -For this chapter, we'll need the following packages attached: - -*Remember to add [any additional packages you need to your course's own docker image](https://github.com/jhudsl/DaSL_Course_Template_Bookdown/wiki/Using-Docker#starting-a-new-docker-image). - - -```r -library(magrittr) -``` - -# Topic of Section - -You can write all your text in sections like this! - -## Subtopic - -Here's a subheading and some text in this subsection! - -### Code examples - -You can demonstrate code like this: - - -```r -output_dir <- file.path("resources", "code_output") -if (!dir.exists(output_dir)) { - dir.create(output_dir) -} -``` - -And make plots too: - - -```r -hist_plot <- hist(iris$Sepal.Length) -``` - - - -You can also save these plots to file: - - -```r -png(file.path(output_dir, "test_plot.png")) -hist_plot -``` - -``` -## $breaks -## [1] 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 -## -## $counts -## [1] 5 27 27 30 31 18 6 6 -## -## $density -## [1] 0.06666667 0.36000000 0.36000000 0.40000000 0.41333333 0.24000000 0.08000000 -## [8] 0.08000000 -## -## $mids -## [1] 4.25 4.75 5.25 5.75 6.25 6.75 7.25 7.75 -## -## $xname -## [1] "iris$Sepal.Length" -## -## $equidist -## [1] TRUE -## -## attr(,"class") -## [1] "histogram" -``` - -```r -dev.off() -``` - -``` -## png -## 2 -``` - -### Image example - - -How to include a Google slide. It's simplest to use the `leanbuild` package: - -Major point!! example image - -But if you have the slide or some other image locally downloaded you can also use html like this: - -Major point!! example image - - -### Video examples - -You can use `knitr::include_url()` like this: - - -```r -knitr::include_url("https://www.youtube.com/embed/VOCYL-FNbr0") -``` - - - -OR this works: - - - -### Links to files - -This works: - - - -Or this: - -[This works](https://www.messiah.edu/download/downloads/id/921/Microaggressions_in_the_Classroom.pdf). - -Or this: - - - -### Links to websites - -Examples of including a website link. - -This works: - - -```r -knitr::include_url("https://yihui.org") -``` - - - -OR this: - -![Another link](https://yihui.org) - -OR this: - - - -### Citation examples - -We can put citations at the end of a sentence like this [@rmarkdown2021]. -Or multiple citations [@rmarkdown2021, @Xie2018]. - -but they need a ; separator [@rmarkdown2021; @Xie2018]. - -In text, we can put citations like this @rmarkdown2021. - -## Print out session info - - -```r -sessionInfo() -``` - -``` -## R version 4.0.2 (2020-06-22) -## Platform: x86_64-pc-linux-gnu (64-bit) -## Running under: Ubuntu 20.04.2 LTS -## -## Matrix products: default -## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so -## -## locale: -## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C -## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 -## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C -## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C -## [9] LC_ADDRESS=C LC_TELEPHONE=C -## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C -## -## attached base packages: -## [1] stats graphics grDevices utils datasets methods base -## -## other attached packages: -## [1] magrittr_1.5 -## -## loaded via a namespace (and not attached): -## [1] knitr_1.33 hms_0.5.3 R6_2.4.1 rlang_0.4.10 -## [5] httr_1.4.2 stringr_1.4.0 highr_0.8 tools_4.0.2 -## [9] xfun_0.26 jquerylib_0.1.1 htmltools_0.5.0 ellipsis_0.3.1 -## [13] yaml_2.2.1 leanbuild_0.1.2 digest_0.6.25 tibble_3.0.3 -## [17] lifecycle_1.0.0 crayon_1.3.4 bookdown_0.24 readr_1.4.0 -## [21] vctrs_0.3.4 fs_1.5.0 curl_4.3 evaluate_0.14 -## [25] rmarkdown_2.10 stringi_1.5.3 compiler_4.0.2 pillar_1.4.6 -## [29] pkgconfig_2.0.3 -``` diff --git a/docs/02-data_types.md b/docs/02-data_types.md deleted file mode 100644 index 5198f60..0000000 --- a/docs/02-data_types.md +++ /dev/null @@ -1,40 +0,0 @@ ---- -title: Clinical Data Types ---- - - - - -# Clinical Data Types - -## Learning Objectives - - -## Patient Records: Electronic Health Records - -Clinical data can come in many different forms, including demographics, diagnoses, lab results, vital signs, medication records, procedures, genetic reports, images, scanned documents, and notes written by physicians, nurses, and other clinicians. Although any of these types of information might theoretically be available for use in clinical research, some sources are more accessible than others. As they are often stored directly in electronic medical record systems, notes, demographics, observations (labs, medications, procedures, and vitals), and images are the easiest data to work with, and are the focus of most Electronic Health Record (EHR) data research efforts. - -### Structured Data - -Observational and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Although details may vary based on the type of EHR being used, the customizations to the EHR for the specific environment in which data was collected, and any pre-processing that might be done by institutional research offices prior to providing data to researchers, details are likely to be familiar across contexts. Structured data types frequently used in EHR research include demographics, diagnoses, lab values, procedures, vitals, and medication records. Often provided as tables indexed off of a patient or visit ID, these tables -often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. - -Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or "billing codes", provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. - -### Unstructured Data / Clinical Notes - -Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. - -When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [INSERT REF HERE] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. - -## Physiological - -## Radiology - -## Pathology - -## Secondary Sources - -## Discussion - -## Conclusion diff --git a/docs/02-data_uses.md b/docs/02-data_uses.md deleted file mode 100644 index 9c47a0f..0000000 --- a/docs/02-data_uses.md +++ /dev/null @@ -1,395 +0,0 @@ ---- -title: Clinical Data Uses ---- - - - -# Clinical Data Uses - -## Learning Objectives - -## General uses of clinical data - -### Cancer Prevention and Care - -The near universal adoption of electronic health record (EHR) systems in the US has created unprecedented opportunities to improve cancer prevention and care. As described in previous chapters, EHR systems store comprehensive longitudinal records of a patient's interactions with a healthcare system, including data about: - - - demographics - - conditions - - family history - - clinical notes - - laboratory test results - - medications - - procedures - - imaging - - genetic test reports - - Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. - -These data can be accessed not only by health professionals, but also by patients through patient portals. EHR data can also be used to enable data-driven interventions such as provider- and patient-facing clinical decision support (CDS) and population health management (PHM). - -CDS has been defined as tools that "provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care" [@Osheroff_2007]. Examples of widely adopted CDS tools with demonstrated effectiveness for cancer prevention, diagnosis, and care include: - - * provider and patient reminders for cancer screening - * decision aids and shared decision-making tools (e.g., for prostate cancer treatment, lung cancer screening) - * AI-supported diagnostic imaging - * chemotherapy decision support - * at-home symptom care - -While CDS tools generally provide decision support focused on a specific patient at a time, PHM are strategies that target specific patient populations [@Swarthout_Bishop_2017]. PHM efforts generally consist of: - - * population algorithms that are applied over EHR and other data sources to identify individuals who are eligible for a specific healthcare intervention (e.g., colorectal cancer screening, tobacco cessation, HPV vaccination) - * patient engagement strategies (e.g., patient portals, text messaging, chatbots, patient navigators) that provide education and access to those interventions - * analytic tools that assess the effectiveness of the PHM program. - -Several PHM programs have demonstrated to be effective in increasing the uptake of cancer prevention. For example, the colorectal cancer screening program at Kaiser Permanente uses digital (i.e., text messaging, patient portal), mailed, and patient navigation approaches to increase colorectal cancer screening by mailing Fecal Immunohistochemical Test (FIT) kits to patients' homes [@Gupta2020]. Also the Cancer Moonshot BRIDGE trial used the GARDE platform [@Bradshaw2022] (ITCR-funded) to identify candidates for genetic testing of hereditary cancer syndromes based on EHR data; and for patient outreach, pre- and post-test education via automated chatbots [@Kaphingst2024]. - - -Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. - -Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. - -While some CDS and PHM approaches have been successfully adopted widely, emerging technologies such as the use of generative AI approaches to analyze diagnostic imaging, large language models (LLMs) to extract information from narrative texts (e.g., clinical notes), LLM-based chatbots to communicate with patients, and digital health tools such as home-based sensors are creating unprecedented opportunities for next generation CDS and PHM. These approaches have the potential to enable significant breakthroughs through the implementation of patient-tailored cancer prevention and care at a population scale. Nevertheless, substantial research is needed to ensure effective and fair implementation of these CDS and PHM interventions. - - - -## Types of questions that can be asked with clinical data - -### Risk Prediction - -Risk prediction in clinical research involves using data to assess the likelihood of certain outcomes or events occurring in patients. This could include predicting the risk of developing a particular disease, experiencing a specific complication, or responding to a treatment. - -Data used for risk prediction can come from various sources, including: - - * Clinical Data: This includes patient demographics, medical history, laboratory results, and imaging studies. - * Genetic Data: Genetic information, such as DNA sequencing results, can provide valuable insights into an individual's susceptibility to certain diseases. - * Environmental and Lifestyle Data: Factors such as diet, exercise habits, smoking status, and environmental exposures can influence disease risk and may be included in risk prediction models. - * Biomarkers: Biological markers indicative of disease or physiological processes can be used as predictors in risk models [@Bodaghi_Fattahi_Ramazani_2023]. - -Once relevant data is collected, statistical and machine learning techniques can be applied to develop predictive models. These models aim to identify patterns and relationships within the data that are associated with the outcome of interest. Common techniques include logistic regression, decision trees, random forests, support vector machines, and neural networks. - -After the model is trained on a dataset, it can be validated using independent datasets to assess its performance and generalizability. Once validated, the model can be used to predict risk in new patients based on their individual characteristics and data. - -Clinical prediction rules are a subset of risk prediction models, specific to clinical research. - -Examples of risk prediction models ... - -Risk prediction models are important because ... - -Overall, risk prediction in clinical research allows healthcare professionals to identify individuals at higher risk of certain outcomes, enabling targeted interventions, personalized treatments, and more efficient resource allocation. - -### Cohort identification for research - -Clinical data plays a crucial role in cohort identification for research purposes. Researchers typically use electronic health records (EHRs), medical databases, or registries to identify cohorts based on specific criteria such as age, gender, medical conditions, treatments, medications, and outcomes. Advanced data mining and natural language processing techniques can also be employed to extract relevant information from unstructured data sources like clinical notes. Once cohorts are identified, researchers can analyze the data to study disease progression, treatment effectiveness, and outcomes. - -Cohort identification is important regardless of research study type, but to provide specific examples: - -* Research Design: Identifying cohorts allows researchers to design studies with appropriate inclusion or exclusion criteria. By selecting specific groups of individuals with similar characteristics or exposures, researchers can investigate hypotheses effectively. -* Clinical Insights: Cohort studies enable researchers to observe the natural history of diseases, track outcomes over time, and assess the effectiveness of interventions or treatments. Understanding how different factors influence disease progression or treatment response can inform clinical decision-making and improve patient care. -* Epidemiological Studies: Cohort identification is crucial for epidemiological research to understand the incidence, prevalence, and risk factors associated with diseases. By following cohorts over time, researchers can identify trends, patterns, and associations that contribute to our understanding of disease causation and prevention. -* Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. -* Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. - -### Case report forms (CRFs) - -In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter -- specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). - -Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on **accessibility, sensitivity, specificity, and comfort for people** - -::: {.notice} - -While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren't necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions -- the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. - -::: - -Here, we explore the different categories of questions that may be included within case report forms: - -1. **Demographic and Socioeconomic Questions** - -Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. - -- **Sex and Gender**: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. -- **Ethnicity and Race**: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. -- **Socioeconomic Status**: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, "What is your current employment status?" with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. - -It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. - -2. **Health and Medial History Questions** - -Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. - -- **Medical Conditions and History**: Questions about past and present health conditions should use clear, accessible language. For example, "Have you ever been diagnosed with any of the following conditions? (Please check all that apply)" followed by a comprehensive list containing all necessary options. -- **Medication Use**: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. -- **Disability and Functional Status**: For many populations, it is important to use person-first language, such as "Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?" and provide space for participants to describe their specific needs. However, different populations have different preferences. The "Disability Language Style Guide" from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic [@ncdj_style_guide]. - -Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. - -3. **Experience and Quality of Life Questions** - -Understanding how health conditions and treatments affect participants' daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. - -- **Daily Living and Social Functioning**: Questions like "How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?" can help assess the impact on daily life, with options ranging from "never" to "always." -- **Emotional and Psychological Well-Being**: Including questions such as "In the past week, how often have you felt anxious or depressed?" using a scale from "not at all" to "very often" can provide insights into mental health needs. -- **Support Systems and Social Networks**: Asking about social support (e.g., "Do you have someone you can rely on for emotional support?") can help identify participants' needs for social and emotional resources. - -Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. - -4. **Sexual and Reproductive Health Questions** - -Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. - -- **Sexual Orientation and Gender Identity (SOGI)**: Instead of just predefined categories, open-ended questions like "How would you describe your sexual orientation?" and "What is your gender identity?" allow participants to self-identify. Offering the option to skip these questions respects participants' privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. -- **Reproductive Health**: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, "Are you currently using any form of contraception? If yes, please specify." -- **Sexual Activity and History**: Questions should be direct but framed sensitively, such as "Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs." - -These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. - -5. **Treatment Preferences and Decision-Making Questions** - -Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. - -- **Decision-Making Preferences**: Questions like "How involved would you like to be in decisions about your healthcare?" offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. -- **Cultural and Religious Considerations**: Asking, "Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?" ensures that care is respectful and culturally appropriate. -- **Treatment Burden**: Questions such as "What level of inconvenience or side effects would be acceptable to you when considering a treatment?" help to gauge participants' preferences and comfort levels. - -These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. - -6. **Accessibility and Accommodation Needs Questions** - -To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. - -- **Language and Communication Needs**: "What is your preferred language for communication? Do you need an interpreter or translated materials?" These questions help ensure that participants can understand the materials. -- **Physical Accessibility**: Asking, "Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?" ensures physical accessibility. -- **Format Preferences**: "Would you prefer to complete this form online, on paper, or verbally with assistance?" helps accommodate different needs and preferences. -Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. - -Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. - -7. **Cultural Sensitivity and Identity Questions** - -CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. - -- **Cultural Identity and Practices**: An open-ended question such as "Are there any cultural practices or beliefs that are important for us to be aware of in your care?" allows participants to share relevant information. -- **Dietary Restrictions and Preferences**: Asking, "Do you have any dietary restrictions or preferences that are culturally or religiously motivated?" ensures that these are respected. -- **Community and Belonging**: "Is there anything about your community or background that you would like us to know to provide better care?" encourages participants to share relevant aspects of their identity. - -These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. - -8. **Ensuring Comfort, Trust, and Privacy Questions** - -Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. - -- **Comfort and Confidentiality**: Asking, "Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?" helps build trust. -- **Feedback and Preferences**: "Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?" invites participants to share their feedback. -- **Consent and Voluntary Participation**: Questions like, "Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary." reinforce autonomy and respect. - -Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. - -::: {.warning} - -The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. - -::: - -Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. By thoughtfully addressing both unique study requirements and universally relevant data points, researchers can optimize CRFs for consistency across clinical studies. - - -### Clinical studies and trials - -Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. - -Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. - -::: {.definition} -The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." -::: - -The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. - -::: {.definition} -**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. -::: - -Here, we explore the different categories of questions that can be addressed using clinical trials data, ranging from the evaluation of treatment efficacy and safety to the exploration of predictive factors for disease prognosis and the personalization of medical care. - -1. **Efficacy and Effectiveness Questions** - -One of the primary objectives of clinical trials is to determine the **efficacy of new treatments or interventions**. Clinical data is used to answer fundamental questions such as "Is the new treatment effective?" and "How does its effectiveness compare to existing treatments?" - -Researchers design studies to measure the **magnitude of treatment effects**, comparing outcomes between treatment and control groups (e.g., a new drug versus a placebo or standard care). - -Beyond controlled settings, data from real-world evidence (RWE) studies can further address questions about the effectiveness of treatments when applied in routine clinical practice. For example, "Is Drug A more effective than Drug B in managing hypertension in a real-world patient population?" Such questions help to assess how treatments perform outside the controlled environment of clinical trials where patient adherence comorbidities, and polypharmacy may influence outcomes. While clinical trials are an important source of real-world data (RWD), they are typically conducted under tightly controlled conditions, which may not fully reflect the complexities of real-world studies. This makes RWE particularly valuable for understanding how treatments work when patients have varying levels of adherence or multiple chronic conditions that are often excluded from clinical trails. - -::: {.definition} -**Comorbidities**: refer to the presence of one or more additional medical conditions or diseases that coexist with a primary condition in a patient. These conditions can occur independently or be related to the primary disease. -::: - -2. **Safety and Tolerability Questions** - -Safety is a critical aspect of any clinical study, and clinical data is essential in identifying and characterizing the safety profile of new treatments. Questions like "What are the common adverse effects of the new treatment?" or "What is the incidence of serious adverse events (SAEs) in patients receiving Drug A?" are addressed by carefully monitoring and recording adverse events throughout the study. Comparative safety questions also arise, such as "Does Drug A have fewer adverse effects compared to Drug B?" These questions are crucial in weighing the benefits of a treatment against its risk and in identifying specific populations that may be at higher risk of adverse reactions. - -::: {.definition} -**Serious Adverse Events (SAEs)**: Any medical occurrence during a clinical trial or medical treatment that results in significant negative outcomes, such as death, life-threatening situations, hospitalization (or its prolongation), persistent or significant disability, or congenital anomalies. SAEs also include any event that requires intervention to prevent one of these outcomes. -::: - -::: {.definition} -**Incidence**: Refers to the rate or number of new cases of SAEs that occur within a specific population during a defined time period, typically during a clinical trial or study. Is it usually expressed as a proportion, such as the number of SAEs per 100 or 1,000 participants, and helps measure how frequently these serious events happen among those exposed to a particular treatment or intervention. -::: - -3. **Comparative Effectiveness and Cost-Effectiveness Questions** - -Beyond safety and efficacy, clinical data can be used to evaluate the comparative effectiveness of different treatments and their cost-effectiveness. These questions often guide healthcare policy and clinical guidelines: "Is Drug A more cost-effective than standard care for managing chronic heart failure?" and "What is the cost per quality-adjusted life year (QALY) gained with the new intervention?" Such analyses provide valuable information for stakeholders, including healthcare providers, payers, and policymakers, to make informed decisions on resource allocation. - -::: {.definition} -**Cost per quality-adjusted life year (QALY)** is a metric used in health economics to assess the value of a medical intervention by measuring the cost of gaining one year of life adjusted for its quality. A QALY incorporates both the **quantity** and **quality** of life lived, where one QALY is equivalent to one year of life in perfect health. If a treatment improves both the length and quality of life, it earns more QALYs. The **cost per QALY** is calculated by dividing the cost of the treatment by the number of QALYs gained, helping to determine the cost-effectiveness of healthcare interventions. Lower cost per QALY indicates better value for money in terms of health benefits achieved [-@Drummond_Sculpher_Claxton_Stoddart_Torrance_2015]. -::: - -4. **Mechanistic and Biomarker Questions** - -Clinical studies often explore not just whether a treatment works, but also how it works. Mechanistic questions delve into the underlying biological pathways affected by a treatment. For instance, "Does Drug A work by inhibiting a specific enzyme involved in the disease process?" Biomarker data can play a key role in such analyses, helping to identify molecular markers that predict response to treatment. Questions like "Are there biomarkers that can predict which patients are more likely to benefit from Drug A?" help in developing targeted therapies and personalized treatment approaches. - -5. **Predictive and Prognostic Questions** - -Clinical trials and studies frequently aim to identify factors that predict disease outcomes or treatment responses. For example, "What baseline characteristics predict a better response to Drug A?" or "What are the predictors of mortality in patients with severe heart failure?" Such prognostic and predictive questions are crucial for identifying high-risk patients, guiding treatment decisions, and developing clinical guidelines. Identifying patient subgroups that benefit more or less from a treatment also facilitates personalized medicine approaches, ensuring that the right treatment is delivered to the right patient at the right time. - -With the advent of personalized medicine, clinical data is increasingly being used to answer questions tailored to individual patient profiles. For instance, "What patient characteristics modify the effect of the treatment?" or "Can we predict which patients are most likely to benefit from Drug A using machine learning models?" These questions are at the forefront of precision medicine, where the goal is to customize healthcare to each patient based on their unique genetic, biomarker, and clinical characteristics. - -6. **Quality of Life and Patient-Reported Outcome Questions** - -In modern clinical research, the impact of treatments on patients' quality of life and their subjective experiences has gained prominence. Questions such as "How does Drug A affect patients' quality of life compared to standard care?" or "What are the patient-reported outcomes associated with the new intervention?" are increasingly addressed in clinical trials. Data from quality of life assessments which are standardized and validated questionnaires (for example, the Health Assessment Questionnaire or HAQ), patient-reported outcome measures (PROMs, tools or questionnaires used to assess a patient's health status or health-related quality of life directly from the patient's perspective), and other patient-centric endpoints (Smoking History, Surgical History, etc.) provide valuable insights into how treatments affect patients beyond traditional clinical endpoints. - -7. **Longitudinal and Follow-Up Questions** - -Long-term follow-up studies are essential for understanding the durability of treatment effects and any delayed adverse effects. Questions like "What are the long-term outcomes associated with Drug A?" and "Is there a sustained benefit of Drug A in reducing symptoms after five years?" are vital for assessing the overall value of treatments. Such longitudinal analyses help in determining the optimal duration of therapy, the need for maintenance treatments, and the overall benefit-risk profile of an intervention. - -8. **Safety in Special Populations Questions** - -Clinical data can also be used to evaluate the safety and efficacy of treatments in special populations, such as children, pregnant women, or elderly patients. Questions such as "Is Drug A safe for use in pregnant women?" and "What is the risk of adverse events when Drug A is administered to patients with multiple comorbidities?" are essential for developing age- and condition-specific clinical guidelines. These questions help ensure that treatments are tailored to the needs of different patient populations, minimizing harm and maximizing benefits. - -9. **Adherence and Compliance Questions** - -Patient adherence to prescribed treatment regimens can significantly impact the outcomes of clinical studies. Questions like "What factors influence patient adherence to the new treatment?" and "How does adherence affect treatment efficacy?" are addressed through analyzing adherence data through monitors if a regulated study, or with interim/baseline analysis if non-regulated. Understanding these factors can lead to interventions that improve adherence and, consequently, the effectiveness of the treatment. - -10. **Real-World Evidence and Generalizability Questions** - -Real-world evidence (RWE) studies help bridge the gap between clinical trial results and clinical practice. Questions such as "How generalizable are the results of this clinical trial to the broader population?" or "What is the impact of real-world use patterns on treatment outcomes?" are essential for understanding how well clinical trial findings apply to everyday clinical settings. These questions help determine if the benefits observed in clinical trials can be replicated in diverse patient populations under routine care conditions. - -11. **Prevention and Risk Reduction Questions** - -Clinical data is also crucial in answering questions related to prevention and risk reduction strategies. For example, "Can the new treatment reduce the risk of developing diabetes in high-risk individuals?" or "What factors are associated with a reduced risk of cardiovascular disease in a large cohort study?" These questions are fundamental in preventive medicine, guiding public health interventions and informing clinical practice. - - -Clinical Data provides a wealth of information that can be leveraged to answer a broad array of questions in clinical trials and studies. From understanding treatment efficacy and safety to exploring long-term outcomes, quality of life impacts, and the potential for personalized medicine, clinical data is foundational to advancing medical knowledge and improving patient care. Case report forms (CRFs) are instrumental in identifying patient cohorts or subgroups, documenting baseline characteristics, and capturing information on comorbidities, while also monitoring adherence data, which are essential for ensuring accurate and meaningful analysis. The type of questions that can be asked and answered are continually evolving as new data sources, analytical methods, and research paradigms emerge, further enriching the field of clinical research. - - -### Retrospective analysis - -Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. - -Here, we explore the different categories of questions that can be addressed using retrospective data analysis: - -1. **Descriptive Questions** - -Descriptive questions aim to summarize and describe the characteristics of a patient population, disease, or healthcare process. They provide a foundational understanding of a dataset and are often the starting point for more complex analyses. - -- **Patient Demographics and Characteristics**: What are the demographic profiles (age, gender, ethnicity, socioeconomic status) of patients diagnosed with a specific condition? What are the common comorbidities and risk factors in this population? -- **Disease Prevalence and Incidence**: What is the prevalence or incidence of a specific disease or condition in a particular population or geographic area over a defined period? -- **Clinical Presentation**: What are the most common presenting symptoms, clinical signs, or laboratory findings associated with a specific disease or condition? - -These questions help in understanding the baseline characteristics and epidemiology of diseases, which is crucial for planning further studies or developing public health interventions. - -2. **Treatment and Intervention Questions** - -Retrospective data can be used to examine the real-world effectiveness, safety, and patterns of treatment and interventions. These questions are critical for understanding how treatments are applied in clinical practice and their outcomes: - -- **Treatment Effectiveness**: What is the effectiveness of a specific treatment or intervention in reducing symptoms, improving quality of life, or achieving clinical outcomes in a real-world setting? -- **Adherence and Persistence**: What are the rates of adherence and persistence with a prescribed treatment regimen over time? What factors are associated with higher or lower adherence rates? -- **Comparative Effectiveness**: How does the effectiveness of one treatment compare to another in a similar population? What are the relative benefits and risks associated with different treatment options? - -By examining historical data, researchers can gain insights into how different treatments perform outside the controlled environment of clinical trials, thereby informing clinical decision-making and guidelines. - -3. **Outcome and Prognostic Questions** - -Understanding patient outcomes and prognostic factors is central to retrospective analyses. These questions focus on the end results of healthcare practices and patient management, including survival, complications, and quality of life: - -- **Survival and Mortality**: What are the survival rates and mortality rates associated with a particular disease, condition, or treatment? What factors are associated with increased or decreased survival rates? -- **Complication Rates**: What are the rates and types of complications associated with specific diseases, surgeries, or treatments? Are there identifiable risk factors for these complications? -- **Prognostic Factors**: What are the key prognostic factors (e.g., age, stage of disease, comorbidities) that influence the outcomes of patients with a particular condition? - -These questions help identify factors that influence patient outcomes, guiding clinicians in predicting disease progression and tailoring individual treatment plans. - -4. Risk Factor and Predictive Modeling Questions - -Retrospective analyses are often used to identify potential risk factors for diseases and develop predictive models. These questions aim to uncover associations between variables and outcomes to help predict future events: - -- **Risk Factors for Disease**: What are the risk factors associated with developing a particular disease or condition? Are there demographic, genetic, behavioral, or environmental factors that significantly increase the risk? -- **Prediction Models**: Can we develop a predictive model to estimate the likelihood of disease progression, relapse, or adverse outcomes based on historical patient data? -- **Multivariable Analysis**: How do multiple factors interact to influence the risk of an outcome? Are there synergistic or antagonistic effects between different risk factors? - -These questions are critical for developing tools that help clinicians assess risk and make more informed decisions in preventive care and early intervention. - -5. **Healthcare Utilization and Cost Questions** - -Understanding how healthcare resources are utilized and the associated costs is important for optimizing healthcare delivery. Retrospective data can provide valuable insights into patterns of care and resource allocation for questions such as: - -- **Healthcare Resource Utilization**: What are the patterns of healthcare utilization (e.g., hospitalizations, emergency room visits, outpatient visits) for patients with a specific condition? How does utilization vary by patient demographics, disease severity, or geographic location? -- **Cost of Care**: What are the direct and indirect costs associated with managing a specific disease or condition? How do different treatment options impact overall healthcare costs? -- **Cost-Effectiveness**: How does the cost-effectiveness of one treatment or intervention compare to another? What are the economic impacts of different clinical management strategies? - -These questions can inform healthcare policy, budget planning, and the development of strategies to improve cost-effectiveness and access to care. - -6. **Quality of Care and Patient Safety Questions** - -Retrospective analysis can be used to evaluate the quality and safety of care provided to patients, identifying areas for improvement and potential patient safety concerns, helping to answer questions such as: - -- **Quality Indicators**: Quality indicators are standardized measures of health care quality and may focus on more specialized groups of care like preventative, inpatient or emergency care, pediatric or maternal health, etc. What are the levels of observed mortality or morbidity for a certain population in a specific setting? How utilized are certain procedures? [@AHRQ] -- **Adverse Events and Safety**: What is the incidence of adverse events, medication errors, or healthcare-associated infections in a particular patient population? What factors are associated with higher or lower rates of adverse events? -- **Patient Satisfaction and Experience**: What are the reported levels of patient satisfaction with healthcare services? How does satisfaction vary by demographic or clinical characteristics? - -These questions help in assessing healthcare quality, identifying gaps in care, and developing interventions to enhance patient safety and satisfaction. - -7. **Temporal and Trend Analysis Questions** - -Retrospective studies often involve analyzing temporal patterns to understand how diseases, treatments, or outcomes change over time. These questions help in recognizing trends and patterns that can inform future research and policy decisions: - -- **Temporal Trends in Disease Incidence and Prevalence**: How have the incidence and prevalence of a specific disease changed over the past decade? Are there seasonal or cyclical patterns in disease occurrence? -- **Trends in Treatment Practices**: How have treatment practices and prescribing patterns evolved over time? Are there emerging trends in the use of certain medications or interventions? -- **Trends in Outcomes and Survival**: How have patient outcomes, such as survival rates or complication rates, changed over time with advances in treatment and healthcare delivery? - -These questions are essential for monitoring public health trends, evaluating the impact of interventions, and planning future healthcare strategies. - -8. **Subgroup and Stratified Analysis Questions** - -Subgroup analyses can help uncover differences in disease characteristics, treatment responses, and outcomes among different patient groups. This type of analysis is particularly valuable for personalized medicine and addressing questions related to health disparities: - -- **Differences by Demographic Group**: How do treatment outcomes, risk factors, or disease characteristics differ between various demographic groups (e.g., age, gender, race/ethnicity)? -- **Subgroup-Specific Effectiveness**: Does a particular treatment work better in one subgroup of patients compared to another? What factors might explain these differences? -- **Health Disparities**: Are there disparities in healthcare access, treatment outcomes, or disease burden across different socioeconomic or minority groups? - -Questions focusing on subgroups are crucial for understanding variations in care and outcomes, identifying at-risk populations, and developing targeted interventions. - - -Retrospective analysis of clinical data allows researchers to ask a wide range of questions that provide valuable insights into patient characteristics, disease epidemiology, treatment effectiveness, outcomes, risk factors, healthcare utilization, and more. By carefully formulating questions and analyzing historical data, researchers can uncover patterns, identify trends, and generate evidence that informs clinical practice, healthcare policy, and future research directions. This chapter outlines the various categories of questions that can be addressed through retrospective analysis, highlighting the potential of this approach to advance knowledge and improve patient care in diverse clinical settings. - - -## Research Design Considerations - -Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. - -One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, - -* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. -* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. -* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. -* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. - -Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: - -* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. -* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. -* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. -* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. - -With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. - -## Conclusion diff --git a/docs/03-data_management.md b/docs/03-data_management.md deleted file mode 100644 index 9ca8c41..0000000 --- a/docs/03-data_management.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: Clinical Data Management ---- - - - -# Clinical Data Management - -## Learning Objectives - -## Clinical data handling tools - -## Importance of keeping clinical data safe - -## Government regulators - -## Documentation - -## Conclusion diff --git a/docs/03-data_uses.md b/docs/03-data_uses.md deleted file mode 100644 index e56a9f8..0000000 --- a/docs/03-data_uses.md +++ /dev/null @@ -1,17 +0,0 @@ ---- -title: Clinical Data Uses ---- - - - -# Clinical Data Uses - -## Learning Objectives - -## General uses of clinical data - -## Types of questions that can be asked with clinical data - -## Research Design Considerations - -## Conclusion diff --git a/docs/04-appendixI.md b/docs/04-appendixI.md deleted file mode 100644 index 193e299..0000000 --- a/docs/04-appendixI.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: Appendix I ---- - - - -# Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards - -## SNOMED - -## ICD10 - -## RxNorm - -## LOINC - -## Emerging standards such as FHIR - -## OMOP diff --git a/docs/04-data_management.md b/docs/04-data_management.md deleted file mode 100644 index 9ca8c41..0000000 --- a/docs/04-data_management.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: Clinical Data Management ---- - - - -# Clinical Data Management - -## Learning Objectives - -## Clinical data handling tools - -## Importance of keeping clinical data safe - -## Government regulators - -## Documentation - -## Conclusion diff --git a/docs/05-analysis_language_data.md b/docs/05-analysis_language_data.md deleted file mode 100644 index 5493604..0000000 --- a/docs/05-analysis_language_data.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -title: Data Analysis of Clinical Language Data ---- - - - -# Data Analysis of Clinical Language Data - -## Learning Objectives - -## Overview of clinical data analysis, language specific - -## Natural Language Processing (NLP) - -## Unified medical language systems (UMLS) - -## Controlled unclassified information (CUIs) - -## LLMs - -## Showcase of ITCR analysis tools - -## Conclusion diff --git a/docs/06-analysis_measures_data.md b/docs/06-analysis_measures_data.md deleted file mode 100644 index 296069e..0000000 --- a/docs/06-analysis_measures_data.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -title: Data Analysis of Clinical Measures Data ---- - - - -# Data Analysis of Clinical Measures Data - -## Learning Objectives - -## Overview of clinical data analysis, measures specific - -## Data cleaning/handling - -## Longitudinal and repeated event data analysis - -## Statistical Methods - -## Tools for both structured and unstructured data - -## Showcase of ITCR analysis tools - -## Patient-facing informatics - -## Common data models & national research initiatives - -## Conclusion diff --git a/docs/07-appendixI.md b/docs/07-appendixI.md deleted file mode 100644 index 193e299..0000000 --- a/docs/07-appendixI.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: Appendix I ---- - - - -# Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards - -## SNOMED - -## ICD10 - -## RxNorm - -## LOINC - -## Emerging standards such as FHIR - -## OMOP diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index 3c977a6..0000000 --- a/docs/404.html +++ /dev/null @@ -1,267 +0,0 @@ - - - - - - - Page not found | Appendix I - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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Robbertz, Ph.D.| -|Content Editor(s)/Reviewer(s) | Julia K. Herriott, B.S.| -|Content Director(s) | Helped guide the content direction| -|Content Consultants (include chapter name/link in parentheses or word "General") - make new line if more than one chapter involved | Gave high level advice on content| -|Acknowledgments| Gave small assistance to content but not to the level of consulting | -|**Production**|| -|Content Publisher(s)| Helped with publishing platform| -|Content Publishing Reviewer(s)| Reviewed overall content and aesthetics on publishing platform| -|**Technical**|| -|Course Publishing Engineer(s)| Helped with the code for the technical aspects related to the specific course generation| -|Template Publishing Engineers|[Candace Savonen](https://www.cansavvy.com/), [Carrie Wright](https://carriewright11.github.io/)| -|Publishing Maintenance Engineer|[Candace Savonen](https://www.cansavvy.com/)| -|Technical Publishing Stylists|[Carrie Wright](https://carriewright11.github.io/), [Candace Savonen](https://www.cansavvy.com/)| -|Package Developers ([Leanbuild](https://github.com/jhudsl/leanbuild))|[John Muschelli](https://johnmuschelli.com/), [Candace Savonen](https://www.cansavvy.com/), [Carrie Wright](https://carriewright11.github.io/)| -|**Art and Design**|| -|Illustrator(s)| Created graphics for the course| -|Figure Artist(s)| Created figures/plots for course| -|Videographer(s)| Filmed videos| -|Videography Editor(s)| Edited film| -|Audiographer(s)| Recorded audio| -|Audiography Editor(s)| Edited audio recordings| -|**Funding**|| -|Funder(s)| Institution/individual who funded course including grant number| -|Funding Staff| Staff members who help with funding| - -  - - -``` -## ─ Session info ─────────────────────────────────────────────────────────────── -## setting value -## version R version 4.3.2 (2023-10-31) -## os Ubuntu 22.04.4 LTS -## system x86_64, linux-gnu -## ui X11 -## language (EN) -## collate en_US.UTF-8 -## ctype en_US.UTF-8 -## tz Etc/UTC -## date 2026-05-10 -## pandoc 3.1.1 @ /usr/local/bin/ (via rmarkdown) -## -## ─ Packages ─────────────────────────────────────────────────────────────────── -## package * version date (UTC) lib source -## bookdown 0.46 2025-12-05 [1] CRAN (R 4.3.2) -## bslib 0.6.1 2023-11-28 [1] RSPM (R 4.3.0) -## cachem 1.0.8 2023-05-01 [1] RSPM (R 4.3.0) -## cli 3.6.5 2025-04-23 [1] CRAN (R 4.3.2) -## devtools 2.4.5 2022-10-11 [1] RSPM (R 4.3.0) -## digest 0.6.34 2024-01-11 [1] RSPM (R 4.3.0) -## ellipsis 0.3.2 2021-04-29 [1] RSPM (R 4.3.0) -## evaluate 1.0.5 2025-08-27 [1] CRAN (R 4.3.2) -## fastmap 1.1.1 2023-02-24 [1] RSPM (R 4.3.0) -## fs 1.6.3 2023-07-20 [1] RSPM (R 4.3.0) -## glue 1.7.0 2024-01-09 [1] RSPM (R 4.3.0) -## htmltools 0.5.7 2023-11-03 [1] RSPM (R 4.3.0) -## htmlwidgets 1.6.4 2023-12-06 [1] RSPM (R 4.3.0) -## httpuv 1.6.14 2024-01-26 [1] RSPM (R 4.3.0) -## jquerylib 0.1.4 2021-04-26 [1] RSPM (R 4.3.0) -## jsonlite 2.0.0 2025-03-27 [1] CRAN (R 4.3.2) -## knitr 1.50 2025-03-16 [1] CRAN (R 4.3.2) -## later 1.3.2 2023-12-06 [1] RSPM (R 4.3.0) -## lifecycle 1.0.4 2023-11-07 [1] RSPM (R 4.3.0) -## magrittr 2.0.3 2022-03-30 [1] RSPM (R 4.3.0) -## memoise 2.0.1 2021-11-26 [1] RSPM (R 4.3.0) -## mime 0.12 2021-09-28 [1] RSPM (R 4.3.0) -## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.3.0) -## pkgbuild 1.4.3 2023-12-10 [1] RSPM (R 4.3.0) -## pkgload 1.4.1 2025-09-23 [1] CRAN (R 4.3.2) -## profvis 0.3.8 2023-05-02 [1] RSPM (R 4.3.0) -## promises 1.2.1 2023-08-10 [1] RSPM (R 4.3.0) -## purrr 1.0.2 2023-08-10 [1] RSPM (R 4.3.0) -## R6 2.6.1 2025-02-15 [1] CRAN (R 4.3.2) -## Rcpp 1.0.12 2024-01-09 [1] RSPM (R 4.3.0) -## remotes 2.4.2.1 2023-07-18 [1] RSPM (R 4.3.0) -## rlang 1.1.6 2025-04-11 [1] CRAN (R 4.3.2) -## rmarkdown 2.25 2023-09-18 [1] RSPM (R 4.3.0) -## sass 0.4.8 2023-12-06 [1] RSPM (R 4.3.0) -## sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.3.0) -## shiny 1.8.0 2023-11-17 [1] RSPM (R 4.3.0) -## stringi 1.8.3 2023-12-11 [1] RSPM (R 4.3.0) -## stringr 1.5.1 2023-11-14 [1] RSPM (R 4.3.0) -## urlchecker 1.0.1 2021-11-30 [1] RSPM (R 4.3.0) -## usethis 2.2.3 2024-02-19 [1] RSPM (R 4.3.0) -## vctrs 0.6.5 2023-12-01 [1] RSPM (R 4.3.0) -## xfun 0.55 2025-12-16 [1] CRAN (R 4.3.2) -## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.3.0) -## yaml 2.3.12 2025-12-10 [1] CRAN (R 4.3.2) -## -## [1] /usr/local/lib/R/site-library -## [2] /usr/local/lib/R/library -## -## ────────────────────────────────────────────────────────────────────────────── -``` - - diff --git a/docs/References.md b/docs/References.md deleted file mode 100644 index 48143a7..0000000 --- a/docs/References.md +++ /dev/null @@ -1,3 +0,0 @@ - -# References {-} - diff --git a/docs/about-the-authors.html b/docs/about-the-authors.html deleted file mode 100644 index 78a94b2..0000000 --- a/docs/about-the-authors.html +++ /dev/null @@ -1,488 +0,0 @@ - - - - - - - About the Authors | Clinical Data Analysis for Cancer Informatics - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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CreditsNames
Pedagogy
Lead Content Instructor(s)FirstName LastName
Lecturer(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involvedDelivered the course in some way - video or audio
Content Author(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involvedIf any other authors besides lead instructor
Content Contributor(s) (include section name/link in parentheses) - make new line if more than one section involvedJennifer Kelleher, Ph.D.1*; Abigail S. Robbertz, Ph.D.
Content Editor(s)/Reviewer(s)Julia K. Herriott, B.S.
Content Director(s)Helped guide the content direction
Content Consultants (include chapter name/link in parentheses or word “General”) - make new line if more than one chapter involvedGave high level advice on content
AcknowledgmentsGave small assistance to content but not to the level of consulting
Production
Content Publisher(s)Helped with publishing platform
Content Publishing Reviewer(s)Reviewed overall content and aesthetics on publishing platform
Technical
Course Publishing Engineer(s)Helped with the code for the technical aspects related to the specific course generation
Template Publishing EngineersCandace Savonen, Carrie Wright
Publishing Maintenance EngineerCandace Savonen
Technical Publishing StylistsCarrie Wright, Candace Savonen
Package Developers (Leanbuild)John Muschelli, Candace Savonen, Carrie Wright
Art and Design
Illustrator(s)Created graphics for the course
Figure Artist(s)Created figures/plots for course
Videographer(s)Filmed videos
Videography Editor(s)Edited film
Audiographer(s)Recorded audio
Audiography Editor(s)Edited audio recordings
Funding
Funder(s)Institution/individual who funded course including grant number
Funding StaffStaff members who help with funding
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Chapter 6 Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards

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6.5 Emerging standards such as FHIR

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OR this works:

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2.1.6 Citation examples

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We can put citations at the end of a sentence like this (Allaire et al. 2021). -Or multiple citations Xie, Allaire, and Grolemund (2018).

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but they need a ; separator (Allaire et al. 2021; Xie, Allaire, and Grolemund 2018).

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In text, we can put citations like this Allaire et al. (2021).

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References

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-Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2021. Rmarkdown: Dynamic Documents for r. https://github.com/rstudio/rmarkdown. -
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-Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown. -
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Chapter 6 Data Analysis of Clinical Language Data

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6.1 Learning Objectives

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6.2 Overview of clinical data analysis, language specific

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6.3 Natural Language Processing (NLP)

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6.4 Unified medical language systems (UMLS)

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6.5 Controlled unclassified information (CUIs)

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6.6 LLMs

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6.7 Showcase of ITCR analysis tools

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6.8 Conclusion

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Chapter 7 Data Analysis of Clinical Measures Data

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7.3 Data cleaning/handling

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7.4 Longitudinal and repeated event data analysis

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7.5 Statistical Methods

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7.7 Showcase of ITCR analysis tools

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7.8 Patient-facing informatics

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7.9 Common data models & national research initiatives

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7.10 Conclusion

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About this Course

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This course is part of a series of courses created for the Informatics Technology for Cancer Research (ITCR) Training Network (ITN). The ITN is a collaborative effort of researchers around the United States that supports cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: National Cancer Institute (NCI) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our website at itcrtraining.org for more information. Except where otherwise indicated, the contents of this course are available for use under the Creative Commons Attribution 4.0 license. You are free to adapt and share the work, but you must give appropriate credit, provide a link to the license, and indicate if changes were made. Sample attribution: Cancer Data Management by Fred Hutchinson Data Science Lab (CC-BY 4.0). You can download the illustrations by clicking on this link for our slides.

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Chapter 2 Introduction

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2.1 How clinical data differs from other data types

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Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data, clinical data is often heterogeneous, integrating quantitative measurements, categorical data, subjective narratives from patient notes, and objective observations from doctor notes and even possible image analysis. Free text adds a layer of complexity with unstructured information, reflecting subjective patient experiences or qualitative insights from healthcare professionals. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data’s unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare.

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2.2 Major clinical data types

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2.3 Specific types of clinical data

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2.4 How to acquire clinical data

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2.5 Description of data

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2.5.1 File types

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2.5.2 Metadata

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2.6 What questions can be asked with clinical data?

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Chapter 1 Introduction

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Clinical Data Management, Written and assembled by: Kate Isaac & Carrie Wright, Contributors: Christopher Battiston, David Hanauer, Guilherme Del Fiol, Harry Hochheiser, Jennifer Kelleher, Abby Robbertz, & Meghan McGrady

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1.1 Motivation

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This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines.

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This course is intended to provide an overview of the different kinds of data commonly used in clinical research, the questions that can typically be asked with such data, as well as guidance for how to manage this data.

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For individuals who: Have clinical data and don’t know what to do with it. Want a basic overview of what the clinical data represent and what questions can be asked.Need to know the basics of how to manage clinical data properly

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1.2 Topics Covered

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Concepts discussed in Clinical Data course: What does your clinical data type represent? How do you handle the clinical data securely and ethically? What types of analysis questions can one ask when it comes to different kinds of clinical data? Find resources, tools and tutorials to help you process and work with your data

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1.3 Curriculum

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The course will cover key underlying principles and concepts in ethical data handling.

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Overall Course Learning Objectives. This course will demonstrate how to: List common sources and types of clinical data, Explain the unique aspects related to clinical data, Understand uses and the questions that can be asked with the major types of clinical data,Recognize major tools and methods for managing clinical data ethically and securely

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- - - - - - - - - - - - - diff --git a/docs/libs/anchor-sections-1.1.0/anchor-sections-hash.css b/docs/libs/anchor-sections-1.1.0/anchor-sections-hash.css deleted file mode 100644 index b563ec9..0000000 --- a/docs/libs/anchor-sections-1.1.0/anchor-sections-hash.css +++ /dev/null @@ -1,2 +0,0 @@ -/* Styles for section anchors */ -a.anchor-section::before {content: '#';font-size: 80%;} diff --git a/docs/libs/anchor-sections-1.1.0/anchor-sections.css b/docs/libs/anchor-sections-1.1.0/anchor-sections.css deleted file mode 100644 index 041905f..0000000 --- a/docs/libs/anchor-sections-1.1.0/anchor-sections.css +++ /dev/null @@ -1,4 +0,0 @@ -/* Styles for section anchors */ -a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;} -.hasAnchor:hover a.anchor-section {visibility: visible;} -ul > li > .anchor-section {display: none;} diff --git a/docs/libs/anchor-sections-1.1.0/anchor-sections.js b/docs/libs/anchor-sections-1.1.0/anchor-sections.js deleted file mode 100644 index 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div.proposition, div.conjecture { - font-style: italic; -} -span.theorem, span.lemma, span.corollary, span.proposition, span.conjecture { - font-style: normal; -} -div.proof>*:last-child:after { - content: "\25a2"; - float: right; -} -.header-section-number { - padding-right: .5em; -} -#header .multi-author { - margin: 0.5em 0 -0.5em 0; -} -#header .date { - margin-top: 1.5em; -} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css b/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css deleted file mode 100644 index 6844a70..0000000 --- a/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css +++ /dev/null @@ -1,18 +0,0 @@ -div.sourceCode { - position: relative; -} - -.copy-to-clipboard-button { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.copy-to-clipboard-button:focus { - outline: 0; -} - -div.sourceCode:hover > .copy-to-clipboard-button { - visibility: visible; -} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css b/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css deleted file mode 100644 index 3fa6f35..0000000 --- a/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css +++ /dev/null @@ -1,303 +0,0 @@ -/* - * Theme 1 - */ -.color-theme-1 .dropdown-menu { - background-color: #111111; - border-color: #7e888b; -} -.color-theme-1 .dropdown-menu .dropdown-caret .caret-inner { - border-bottom: 9px solid #111111; -} -.color-theme-1 .dropdown-menu .buttons { - border-color: #7e888b; -} -.color-theme-1 .dropdown-menu .button { - color: #afa790; -} -.color-theme-1 .dropdown-menu .button:hover { - color: #73553c; -} -/* - * Theme 2 - */ -.color-theme-2 .dropdown-menu { - background-color: #2d3143; - border-color: #272a3a; -} -.color-theme-2 .dropdown-menu .dropdown-caret .caret-inner { - border-bottom: 9px solid #2d3143; -} -.color-theme-2 .dropdown-menu .buttons { - border-color: #272a3a; -} -.color-theme-2 .dropdown-menu .button { - color: #62677f; -} -.color-theme-2 .dropdown-menu .button:hover { - color: #f4f4f5; -} -.book .book-header .font-settings .font-enlarge { - line-height: 30px; - font-size: 1.4em; -} -.book .book-header .font-settings .font-reduce { - line-height: 30px; - font-size: 1em; -} - -/* sidebar transition background */ -div.book.color-theme-1 { - background: #f3eacb; -} -.book.color-theme-1 .book-body { - color: #704214; - background: #f3eacb; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section { - background: #f3eacb; -} - -/* sidebar transition background */ -div.book.color-theme-2 { - background: #1c1f2b; -} - -.book.color-theme-2 .book-body { - color: #bdcadb; - background: #1c1f2b; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section { - background: #1c1f2b; -} -.book.font-size-0 .book-body .page-inner section { - font-size: 1.2rem; -} -.book.font-size-1 .book-body .page-inner section { - font-size: 1.4rem; -} -.book.font-size-2 .book-body .page-inner section { - font-size: 1.6rem; -} -.book.font-size-3 .book-body .page-inner section { - font-size: 2.2rem; -} -.book.font-size-4 .book-body .page-inner section { - font-size: 4rem; -} -.book.font-family-0 { - font-family: Georgia, serif; -} -.book.font-family-1 { - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal { - color: #704214; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal a { - color: inherit; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h1, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h2, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h3, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h4, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h5, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h6 { - color: inherit; -} -.book.color-theme-1 .book-body .page-wrapper 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section.normal table td { - border-color: #3b3f54; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table tr { - color: #b6c2d2; - background-color: #2d3143; - border-color: #3b3f54; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table tr:nth-child(2n) { - background-color: #35394b; -} -.book.color-theme-1 .book-header { - color: #afa790; - background: transparent; -} -.book.color-theme-1 .book-header .btn { - color: #afa790; -} -.book.color-theme-1 .book-header .btn:hover { - color: #73553c; - background: none; -} -.book.color-theme-1 .book-header h1 { - color: #704214; -} -.book.color-theme-2 .book-header { - color: #7e888b; - background: transparent; -} -.book.color-theme-2 .book-header .btn { - color: #3b3f54; -} -.book.color-theme-2 .book-header .btn:hover { - color: #fffff5; - background: none; -} -.book.color-theme-2 .book-header h1 { - color: #bdcadb; -} -.book.color-theme-1 .book-body .navigation { - color: #afa790; -} 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modifiers=[];if(e.shiftKey){modifiers.push("shift")}if(e.altKey){modifiers.push("alt")}if(e.ctrlKey){modifiers.push("ctrl")}if(e.metaKey){modifiers.push("meta")}return modifiers}function _preventDefault(e){if(e.preventDefault){e.preventDefault();return}e.returnValue=false}function _stopPropagation(e){if(e.stopPropagation){e.stopPropagation();return}e.cancelBubble=true}function _isModifier(key){return key=="shift"||key=="ctrl"||key=="alt"||key=="meta"}function _getReverseMap(){if(!_REVERSE_MAP){_REVERSE_MAP={};for(var key in _MAP){if(key>95&&key<112){continue}if(_MAP.hasOwnProperty(key)){_REVERSE_MAP[_MAP[key]]=key}}}return _REVERSE_MAP}function _pickBestAction(key,modifiers,action){if(!action){action=_getReverseMap()[key]?"keydown":"keypress"}if(action=="keypress"&&modifiers.length){action="keydown"}return action}function _keysFromString(combination){if(combination==="+"){return["+"]}combination=combination.replace(/\+{2}/g,"+plus");return combination.split("+")}function _getKeyInfo(combination,action){var keys;var key;var i;var modifiers=[];keys=_keysFromString(combination);for(i=0;i1){_bindSequence(combination,sequence,callback,action);return}info=_getKeyInfo(combination,action);self._callbacks[info.key]=self._callbacks[info.key]||[];_getMatches(info.key,info.modifiers,{type:info.action},sequenceName,combination,level);self._callbacks[info.key][sequenceName?"unshift":"push"]({callback:callback,modifiers:info.modifiers,action:info.action,seq:sequenceName,level:level,combo:combination})}self._bindMultiple=function(combinations,callback,action){for(var i=0;i-1){return false}if(_belongsTo(element,self.target)){return false}return element.tagName=="INPUT"||element.tagName=="SELECT"||element.tagName=="TEXTAREA"||element.isContentEditable};Mousetrap.prototype.handleKey=function(){var self=this;return self._handleKey.apply(self,arguments)};Mousetrap.init=function(){var documentMousetrap=Mousetrap(document);for(var method in documentMousetrap){if(method.charAt(0)!=="_"){Mousetrap[method]=function(method){return function(){return documentMousetrap[method].apply(documentMousetrap,arguments)}}(method)}}};Mousetrap.init();window.Mousetrap=Mousetrap;if(typeof module!=="undefined"&&module.exports){module.exports=Mousetrap}if(typeof define==="function"&&define.amd){define(function(){return Mousetrap})}})(window,document)},{}],4:[function(require,module,exports){(function(process){function normalizeArray(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up--;up){parts.unshift("..")}}return parts}var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;var splitPath=function(filename){return splitPathRe.exec(filename).slice(1)};exports.resolve=function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:process.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){continue}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=normalizeArray(filter(resolvedPath.split("/"),function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."};exports.normalize=function(path){var isAbsolute=exports.isAbsolute(path),trailingSlash=substr(path,-1)==="/";path=normalizeArray(filter(path.split("/"),function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path};exports.isAbsolute=function(path){return path.charAt(0)==="/"};exports.join=function(){var paths=Array.prototype.slice.call(arguments,0);return exports.normalize(filter(paths,function(p,index){if(typeof p!=="string"){throw new TypeError("Arguments to path.join must be strings")}return p}).join("/"))};exports.relative=function(from,to){from=exports.resolve(from).substr(1);to=exports.resolve(to).substr(1);function trim(arr){var start=0;for(;start=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i1){for(var i=1;i= 0x80 (not a basic code point)","invalid-input":"Invalid input"},baseMinusTMin=base-tMin,floor=Math.floor,stringFromCharCode=String.fromCharCode,key;function error(type){throw RangeError(errors[type])}function map(array,fn){var length=array.length;var result=[];while(length--){result[length]=fn(array[length])}return result}function mapDomain(string,fn){var parts=string.split("@");var result="";if(parts.length>1){result=parts[0]+"@";string=parts[1]}string=string.replace(regexSeparators,".");var labels=string.split(".");var encoded=map(labels,fn).join(".");return result+encoded}function ucs2decode(string){var output=[],counter=0,length=string.length,value,extra;while(counter=55296&&value<=56319&&counter65535){value-=65536;output+=stringFromCharCode(value>>>10&1023|55296);value=56320|value&1023}output+=stringFromCharCode(value);return output}).join("")}function basicToDigit(codePoint){if(codePoint-48<10){return codePoint-22}if(codePoint-65<26){return codePoint-65}if(codePoint-97<26){return codePoint-97}return base}function digitToBasic(digit,flag){return digit+22+75*(digit<26)-((flag!=0)<<5)}function adapt(delta,numPoints,firstTime){var k=0;delta=firstTime?floor(delta/damp):delta>>1;delta+=floor(delta/numPoints);for(;delta>baseMinusTMin*tMax>>1;k+=base){delta=floor(delta/baseMinusTMin)}return floor(k+(baseMinusTMin+1)*delta/(delta+skew))}function decode(input){var output=[],inputLength=input.length,out,i=0,n=initialN,bias=initialBias,basic,j,index,oldi,w,k,digit,t,baseMinusT;basic=input.lastIndexOf(delimiter);if(basic<0){basic=0}for(j=0;j=128){error("not-basic")}output.push(input.charCodeAt(j))}for(index=basic>0?basic+1:0;index=inputLength){error("invalid-input")}digit=basicToDigit(input.charCodeAt(index++));if(digit>=base||digit>floor((maxInt-i)/w)){error("overflow")}i+=digit*w;t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(digitfloor(maxInt/baseMinusT)){error("overflow")}w*=baseMinusT}out=output.length+1;bias=adapt(i-oldi,out,oldi==0);if(floor(i/out)>maxInt-n){error("overflow")}n+=floor(i/out);i%=out;output.splice(i++,0,n)}return ucs2encode(output)}function encode(input){var n,delta,handledCPCount,basicLength,bias,j,m,q,k,t,currentValue,output=[],inputLength,handledCPCountPlusOne,baseMinusT,qMinusT;input=ucs2decode(input);inputLength=input.length;n=initialN;delta=0;bias=initialBias;for(j=0;j=n&¤tValuefloor((maxInt-delta)/handledCPCountPlusOne)){error("overflow")}delta+=(m-n)*handledCPCountPlusOne;n=m;for(j=0;jmaxInt){error("overflow")}if(currentValue==n){for(q=delta,k=base;;k+=base){t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(q0&&len>maxKeys){len=maxKeys}for(var i=0;i=0){kstr=x.substr(0,idx);vstr=x.substr(idx+1)}else{kstr=x;vstr=""}k=decodeURIComponent(kstr);v=decodeURIComponent(vstr);if(!hasOwnProperty(obj,k)){obj[k]=v}else if(isArray(obj[k])){obj[k].push(v)}else{obj[k]=[obj[k],v]}}return obj};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"}},{}],8:[function(require,module,exports){"use strict";var stringifyPrimitive=function(v){switch(typeof v){case"string":return v;case"boolean":return v?"true":"false";case"number":return isFinite(v)?v:"";default:return""}};module.exports=function(obj,sep,eq,name){sep=sep||"&";eq=eq||"=";if(obj===null){obj=undefined}if(typeof obj==="object"){return map(objectKeys(obj),function(k){var ks=encodeURIComponent(stringifyPrimitive(k))+eq;if(isArray(obj[k])){return map(obj[k],function(v){return ks+encodeURIComponent(stringifyPrimitive(v))}).join(sep)}else{return ks+encodeURIComponent(stringifyPrimitive(obj[k]))}}).join(sep)}if(!name)return"";return encodeURIComponent(stringifyPrimitive(name))+eq+encodeURIComponent(stringifyPrimitive(obj))};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"};function map(xs,f){if(xs.map)return xs.map(f);var res=[];for(var i=0;i",'"',"`"," ","\r","\n","\t"],unwise=["{","}","|","\\","^","`"].concat(delims),autoEscape=["'"].concat(unwise),nonHostChars=["%","/","?",";","#"].concat(autoEscape),hostEndingChars=["/","?","#"],hostnameMaxLen=255,hostnamePartPattern=/^[a-z0-9A-Z_-]{0,63}$/,hostnamePartStart=/^([a-z0-9A-Z_-]{0,63})(.*)$/,unsafeProtocol={javascript:true,"javascript:":true},hostlessProtocol={javascript:true,"javascript:":true},slashedProtocol={http:true,https:true,ftp:true,gopher:true,file:true,"http:":true,"https:":true,"ftp:":true,"gopher:":true,"file:":true},querystring=require("querystring");function urlParse(url,parseQueryString,slashesDenoteHost){if(url&&isObject(url)&&url instanceof Url)return url;var u=new Url;u.parse(url,parseQueryString,slashesDenoteHost);return u}Url.prototype.parse=function(url,parseQueryString,slashesDenoteHost){if(!isString(url)){throw new TypeError("Parameter 'url' must be a string, not "+typeof url)}var rest=url;rest=rest.trim();var proto=protocolPattern.exec(rest);if(proto){proto=proto[0];var lowerProto=proto.toLowerCase();this.protocol=lowerProto;rest=rest.substr(proto.length)}if(slashesDenoteHost||proto||rest.match(/^\/\/[^@\/]+@[^@\/]+/)){var slashes=rest.substr(0,2)==="//";if(slashes&&!(proto&&hostlessProtocol[proto])){rest=rest.substr(2);this.slashes=true}}if(!hostlessProtocol[proto]&&(slashes||proto&&!slashedProtocol[proto])){var hostEnd=-1;for(var i=0;i127){newpart+="x"}else{newpart+=part[j]}}if(!newpart.match(hostnamePartPattern)){var validParts=hostparts.slice(0,i);var notHost=hostparts.slice(i+1);var bit=part.match(hostnamePartStart);if(bit){validParts.push(bit[1]);notHost.unshift(bit[2])}if(notHost.length){rest="/"+notHost.join(".")+rest}this.hostname=validParts.join(".");break}}}}if(this.hostname.length>hostnameMaxLen){this.hostname=""}else{this.hostname=this.hostname.toLowerCase()}if(!ipv6Hostname){var domainArray=this.hostname.split(".");var newOut=[];for(var i=0;i0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}result.search=relative.search;result.query=relative.query;if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.href=result.format();return result}if(!srcPath.length){result.pathname=null;if(result.search){result.path="/"+result.search}else{result.path=null}result.href=result.format();return result}var last=srcPath.slice(-1)[0];var hasTrailingSlash=(result.host||relative.host)&&(last==="."||last==="..")||last==="";var up=0;for(var i=srcPath.length;i>=0;i--){last=srcPath[i];if(last=="."){srcPath.splice(i,1)}else if(last===".."){srcPath.splice(i,1);up++}else if(up){srcPath.splice(i,1);up--}}if(!mustEndAbs&&!removeAllDots){for(;up--;up){srcPath.unshift("..")}}if(mustEndAbs&&srcPath[0]!==""&&(!srcPath[0]||srcPath[0].charAt(0)!=="/")){srcPath.unshift("")}if(hasTrailingSlash&&srcPath.join("/").substr(-1)!=="/"){srcPath.push("")}var isAbsolute=srcPath[0]===""||srcPath[0]&&srcPath[0].charAt(0)==="/";if(psychotic){result.hostname=result.host=isAbsolute?"":srcPath.length?srcPath.shift():"";var authInHost=result.host&&result.host.indexOf("@")>0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}mustEndAbs=mustEndAbs||result.host&&srcPath.length;if(mustEndAbs&&!isAbsolute){srcPath.unshift("")}if(!srcPath.length){result.pathname=null;result.path=null}else{result.pathname=srcPath.join("/")}if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.auth=relative.auth||result.auth;result.slashes=result.slashes||relative.slashes;result.href=result.format();return result};Url.prototype.parseHost=function(){var host=this.host;var port=portPattern.exec(host);if(port){port=port[0];if(port!==":"){this.port=port.substr(1)}host=host.substr(0,host.length-port.length)}if(host)this.hostname=host};function isString(arg){return typeof arg==="string"}function isObject(arg){return typeof arg==="object"&&arg!==null}function isNull(arg){return arg===null}function isNullOrUndefined(arg){return arg==null}},{punycode:6,querystring:9}],11:[function(require,module,exports){var $=require("jquery");function toggleDropdown(e){var $dropdown=$(e.currentTarget).parent().find(".dropdown-menu");$dropdown.toggleClass("open");e.stopPropagation();e.preventDefault()}function closeDropdown(e){$(".dropdown-menu").removeClass("open")}function init(){$(document).on("click",".toggle-dropdown",toggleDropdown);$(document).on("click",".dropdown-menu",function(e){e.stopPropagation()});$(document).on("click",closeDropdown)}module.exports={init:init}},{jquery:1}],12:[function(require,module,exports){var $=require("jquery");module.exports=$({})},{jquery:1}],13:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var dropdown=require("./dropdown");var events=require("./events");var state=require("./state");var keyboard=require("./keyboard");var navigation=require("./navigation");var sidebar=require("./sidebar");var toolbar=require("./toolbar");function start(config){sidebar.init();keyboard.init();dropdown.init();navigation.init();toolbar.createButton({index:0,icon:"fa fa-align-justify",label:"Toggle Sidebar",onClick:function(e){e.preventDefault();sidebar.toggle()}});events.trigger("start",config);navigation.notify()}var gitbook={start:start,events:events,state:state,toolbar:toolbar,sidebar:sidebar,storage:storage,keyboard:keyboard};var MODULES={gitbook:gitbook,jquery:$,lodash:_};window.gitbook=gitbook;window.$=$;window.jQuery=$;gitbook.require=function(mods,fn){mods=_.map(mods,function(mod){mod=mod.toLowerCase();if(!MODULES[mod]){throw new Error("GitBook module "+mod+" doesn't exist")}return MODULES[mod]});fn.apply(null,mods)};module.exports={}},{"./dropdown":11,"./events":12,"./keyboard":14,"./navigation":16,"./sidebar":18,"./state":19,"./storage":20,"./toolbar":21,jquery:1,lodash:2}],14:[function(require,module,exports){var Mousetrap=require("mousetrap");var navigation=require("./navigation");var sidebar=require("./sidebar");function bindShortcut(keys,fn){Mousetrap.bind(keys,function(e){fn();return false})}function init(){bindShortcut(["right"],function(e){navigation.goNext()});bindShortcut(["left"],function(e){navigation.goPrev()});bindShortcut(["s"],function(e){sidebar.toggle()})}module.exports={init:init,bind:bindShortcut}},{"./navigation":16,"./sidebar":18,mousetrap:3}],15:[function(require,module,exports){var state=require("./state");function showLoading(p){state.$book.addClass("is-loading");p.always(function(){state.$book.removeClass("is-loading")});return p}module.exports={show:showLoading}},{"./state":19}],16:[function(require,module,exports){var $=require("jquery");var url=require("url");var events=require("./events");var state=require("./state");var loading=require("./loading");var usePushState=typeof history.pushState!=="undefined";function handleNavigation(relativeUrl,push){var uri=url.resolve(window.location.pathname,relativeUrl);notifyPageChange();location.href=relativeUrl;return}function updateNavigationPosition(){var bodyInnerWidth,pageWrapperWidth;bodyInnerWidth=parseInt($(".body-inner").css("width"),10);pageWrapperWidth=parseInt($(".page-wrapper").css("width"),10);$(".navigation-next").css("margin-right",bodyInnerWidth-pageWrapperWidth+"px")}function notifyPageChange(){events.trigger("page.change")}function preparePage(notify){var $bookBody=$(".book-body");var $bookInner=$bookBody.find(".body-inner");var $pageWrapper=$bookInner.find(".page-wrapper");updateNavigationPosition();$bookInner.scrollTop(0);$bookBody.scrollTop(0);if(notify!==false)notifyPageChange()}function isLeftClickEvent(e){return e.button===0}function isModifiedEvent(e){return!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey)}function handlePagination(e){if(isModifiedEvent(e)||!isLeftClickEvent(e)){return}e.stopPropagation();e.preventDefault();var url=$(this).attr("href");if(url)handleNavigation(url,true)}function goNext(){var url=$(".navigation-next").attr("href");if(url)handleNavigation(url,true)}function goPrev(){var url=$(".navigation-prev").attr("href");if(url)handleNavigation(url,true)}function init(){$.ajaxSetup({});if(location.protocol!=="file:"){history.replaceState({path:window.location.href},"")}window.onpopstate=function(event){if(event.state===null){return}return handleNavigation(event.state.path,false)};$(document).on("click",".navigation-prev",handlePagination);$(document).on("click",".navigation-next",handlePagination);$(document).on("click",".summary [data-path] a",handlePagination);$(window).resize(updateNavigationPosition);preparePage(false)}module.exports={init:init,goNext:goNext,goPrev:goPrev,notify:notifyPageChange}},{"./events":12,"./loading":15,"./state":19,jquery:1,url:10}],17:[function(require,module,exports){module.exports={isMobile:function(){return document.body.clientWidth<=600}}},{}],18:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var platform=require("./platform");var state=require("./state");function toggleSidebar(_state,animation){if(state!=null&&isOpen()==_state)return;if(animation==null)animation=true;state.$book.toggleClass("without-animation",!animation);state.$book.toggleClass("with-summary",_state);storage.set("sidebar",isOpen())}function isOpen(){return state.$book.hasClass("with-summary")}function init(){if(platform.isMobile()){toggleSidebar(false,false)}else{toggleSidebar(storage.get("sidebar",true),false)}$(document).on("click",".book-summary li.chapter a",function(e){if(platform.isMobile())toggleSidebar(false,false)})}function filterSummary(paths){var $summary=$(".book-summary");$summary.find("li").each(function(){var path=$(this).data("path");var st=paths==null||_.contains(paths,path);$(this).toggle(st);if(st)$(this).parents("li").show()})}module.exports={init:init,isOpen:isOpen,toggle:toggleSidebar,filter:filterSummary}},{"./platform":17,"./state":19,"./storage":20,jquery:1,lodash:2}],19:[function(require,module,exports){var $=require("jquery");var url=require("url");var path=require("path");var state={};state.update=function(dom){var $book=$(dom.find(".book"));state.$book=$book;state.level=$book.data("level");state.basePath=$book.data("basepath");state.innerLanguage=$book.data("innerlanguage");state.revision=$book.data("revision");state.filepath=$book.data("filepath");state.chapterTitle=$book.data("chapter-title");state.root=url.resolve(location.protocol+"//"+location.host,path.dirname(path.resolve(location.pathname.replace(/\/$/,"/index.html"),state.basePath))).replace(/\/?$/,"/");state.bookRoot=state.innerLanguage?url.resolve(state.root,".."):state.root};state.update($);module.exports=state},{jquery:1,path:4,url:10}],20:[function(require,module,exports){var baseKey="";module.exports={setBaseKey:function(key){baseKey=key},set:function(key,value){key=baseKey+":"+key;try{sessionStorage[key]=JSON.stringify(value)}catch(e){}},get:function(key,def){key=baseKey+":"+key;if(sessionStorage[key]===undefined)return def;try{var v=JSON.parse(sessionStorage[key]);return v==null?def:v}catch(err){return sessionStorage[key]||def}},remove:function(key){key=baseKey+":"+key;sessionStorage.removeItem(key)}}},{}],21:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var events=require("./events");var buttons=[];function insertAt(parent,selector,index,element){var lastIndex=parent.children(selector).length;if(index<0){index=Math.max(0,lastIndex+1+index)}parent.append(element);if(index",{class:"dropdown-menu",html:'

'});if(_.isString(dropdown)){$menu.append(dropdown)}else{var groups=_.map(dropdown,function(group){if(_.isArray(group))return group;else return[group]});_.each(groups,function(group){var $group=$("
",{class:"buttons"});var sizeClass="size-"+group.length;_.each(group,function(btn){btn=_.defaults(btn||{},{text:"",className:"",onClick:defaultOnClick});var $btn=$("'; - var clipboard; - - gitbook.events.bind("page.change", function() { - - if (!ClipboardJS.isSupported()) return; - - // the page.change event is thrown twice: before and after the page changes - if (clipboard) { - // clipboard is already defined but we are on the same page - if (clipboard._prevPage === window.location.pathname) return; - // clipboard is already defined and url path change - // we can deduct that we are before page changes - clipboard.destroy(); // destroy the previous events listeners - clipboard = undefined; // reset the clipboard object - return; - } - - $(copyButton).prependTo("div.sourceCode"); - - clipboard = new ClipboardJS(".copy-to-clipboard-button", { - text: function(trigger) { - return trigger.parentNode.textContent; - } - }); - - clipboard._prevPage = window.location.pathname - - }); - -}); diff --git a/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js b/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js deleted file mode 100644 index e743148..0000000 --- a/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js +++ /dev/null @@ -1,175 +0,0 @@ -gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) { - var fontState; - - var THEMES = { - "white": 0, - "sepia": 1, - "night": 2 - }; - - var FAMILY = { - "serif": 0, - "sans": 1 - }; - - // Save current font settings - function saveFontSettings() { - gitbook.storage.set("fontState", fontState); - update(); - } - - // Increase font size - function enlargeFontSize(e) { - e.preventDefault(); - if (fontState.size >= 4) return; - - fontState.size++; - saveFontSettings(); - }; - - // Decrease font size - function reduceFontSize(e) { - e.preventDefault(); - if (fontState.size <= 0) return; - - fontState.size--; - saveFontSettings(); - }; - - // Change font family - function changeFontFamily(index, e) { - e.preventDefault(); - - fontState.family = index; - saveFontSettings(); - }; - - // Change type of color - function changeColorTheme(index, e) { - e.preventDefault(); - - var $book = $(".book"); - - if (fontState.theme !== 0) - $book.removeClass("color-theme-"+fontState.theme); - - fontState.theme = index; - if (fontState.theme !== 0) - $book.addClass("color-theme-"+fontState.theme); - - saveFontSettings(); - }; - - // Increase or decrease line spacing - function changeSpacing(e, inc = true) { - e.preventDefault(); - inc ? 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The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines. - -This course is intended to provide an overview of the different kinds of data commonly used in clinical research, the questions that can typically be asked with such data, as well as guidance for how to manage this data. - - -For individuals who: Have clinical data and don’t know what to do with it. Want a basic overview of what the clinical data represent and what questions can be asked.Need to know the basics of how to manage clinical data properly - - -## Topics Covered - -Concepts discussed in Clinical Data course: What does your clinical data type represent? How do you handle the clinical data securely and ethically? What types of analysis questions can one ask when it comes to different kinds of clinical data? Find resources, tools and tutorials to help you process and work with your data - - -## Curriculum - -The course will cover key underlying principles and concepts in ethical data handling. - -Overall Course Learning Objectives. 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It may come in many forms and its sensitive nature requires careful management by researchers. - -## Learning Objectives - -Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data - - - -## Clinical data is unique - -Clinical data refers to information __collected from patients__ during healthcare delivery, clinical trials, and medical research. Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data with predictable and consistent formats, clinical data is often heterogeneous, integrating many forms of both structured and unstructured data: quantitative measurements, categorical data, genotyping, images, subjective narratives from patient notes, and objective observations or conclusions. The unstructured nature of free text from notes reflecting subjective patient experiences or qualitative insights from healthcare professionals especially adds a further layer of complexity. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data's unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare. Further, because clinical data contains sensitive, personal information about patients, there are additional security and ethics concerns in the handling and management of clinical data. - -## Major clinical data types - -Clinical data can come in many different forms, including - -* patient demographics -* medical history or records such as diagnoses, lab results, vital signs, medication records, or procedure history -* genetic reports -* health monitor data -* images -* scanned documents, and notes written by physicians, nurses, and other clinicians -* survey/ case report form (CRF responses) -* and more ... - -Clinical data refers to information collected from patients during healthcare delivery, clinical trials, and medical research and could include demographics, medical history, lab results, imaging, treatment outcomes, and more - -Some sources of clinical data are more prevalent and readily obtainable than others. For instance, notes, demographics, images, and histories or observations/records (lab results, vitals, medications, procedures) are often stored directly in electronic medical record systems making them more easily accessible and so are the focus of most Electronic Health Record (EHR) data research efforts. - -### Structured data - -Observational records such as test results and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Structured data types frequently used in EHR research consist of comprehensive longitudinal records of a patient's interactions with a healthcare system and may include demographics, diagnoses, lab values, procedures, vitals, and medication records. - -Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. - -The structured nature of this data allows for it to be stored in tables which may be indexed by a patient or visit ID and often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. - -Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or "billing codes", provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. - -Example of structured data, a table that includes patient id numbers, billing codes, dates, blood pressure measurements, weight measurements, height measurements, and prescribed medications coded using the National Drug Code (NDC) - -As we've described it, structured clinical data is expected to have similarities, although specific details may vary based on the type of EHR being used and any customizations to the EHR for the specific environment or institution in which data was collected (e.g., any specialized pre-processing by institutional research offices prior to providing data to researchers). So it is important for researchers to consider differences present in their data if they've obtained it from multiple contexts or institutions. - -### Unstructured data / clinical notes - -Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. - - -Unstructured Data - Data without specific format: includes images, pathology reports, radiology reports, clinical notes, discharge summaries and more. Includes an image of an x-ray and some clinical notes that states: Patient reports a sharp pain in the right lower abdomen for the past 24 hours.Temperature 98.6°F, BP 120/80, tenderness in the right lower quadrant. Suspected appendicitis. Refer to surgery for evaluation, initiate IV fluids, and pain management. - - -When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [@Dhir2008] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. - - -## How to acquire clinical data - -### Secondary Sources - - -## Description of data - -### File types - -### Metadata - - -## Summary diff --git a/docs/no_toc/01-data_types_files/figure-html/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ_g3385bea4ad0_0_0.png b/docs/no_toc/01-data_types_files/figure-html/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ_g3385bea4ad0_0_0.png deleted file mode 100644 index 4e606c1c720e542f24ed2c679e80038e82d8a9f5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 52457 zcmeFYbyU@B)HTXRP(m7ML_$&;q+!z_-O}CN(kb1oNH+*bOG^kMAe)w!F6r*?;dtJ2 z&O62(sf2AIp=zIxT3ry8VV5#92^{)w3L`K9NdF1aB%P>Nbul4 z^+Jj-;Nbk>q{W0)-{}3$c=Q2BoVbUwm68-Aj-oY)BKO&|k@Q?7DHTlde54U!&qV51 zspx?td?U>+`5#nE=)ciWggsL`5?DFuY3+O7kUq2WD)Vmdt-jmZ+*B)h`*Xx=fB5%*4nvpzhnQ1gxoY;MfC4@CP>1+C;B_e 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Robbertz, Ph.D.^1^; and Meghan E. McGrady, Ph.D.^1,2^ - -**NOTE:** Jennifer Kelleher, Ph.D.^1^ and Abigail S. Robbertz, Ph.D.^1^ contributed equally. - -^1^ Center for Adherence and Self-Management, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA - -^2^ Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA - -The work discussed in this section was also supported by the National Cancer Institute at the National Institutes of Health (R21CA263704, K07CA200668) to MEM. JK and ASR are supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development at the National Institutes of Health (T32HD068223). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. - -::: - -Electronic monitoring devices are digital tools that can be used to track health behaviors over time such as: - -* Sleep -* Physical activity -* Medication adherence -* Calorie intake - -Electronic monitoring devices can also be used to assess physical health indicators including: -* Blood glucose levels -* Blood pressure -* Heart rate and heart rate variability -* Oxygen saturation - -Electronic monitoring devices enable researchers to track day-to-day health behaviors in the patient’s **"real-world" setting**. This allows researchers to explore patterns or changes in a patient’s health behavior and provides a richer understanding of **daily behavior over time**. - - -### Benefits of Monitoring Data - -1) Electronic monitoring devices often include data transmission abilities that enable healthcare providers or researchers to access these data in near **real-time** potentially informing intervention and/or medical decision-making. - -1) Electronic monitoring devices also have the potential to produce **more accurate** estimates of health behaviors than alternative strategies (e.g., self-report) as they are not subject to recall bias and can detect efforts to inflate adherence due to social desirability. - -### Considerations - -:::warning -This section is not exhaustive. Research teams are strongly encouraged to consult with experts with experience and training in collecting and analyzing data from specific devices. - -To ensure the outcome variables are aligned with the research question of interest and [ethical and age/developmental considerations](https://pmc.ncbi.nlm.nih.gov/articles/PMC10798216/) (@psihogios_ethical_2024; @modi_pediatric_2012) have been appropriately accounted for, readers are encouraged to consult with researchers in their field who have integrated these measurement strategies into their work. -::: - -#### Medication Adherence - -There are three major components of medical adherence (the tracking of taking medication): - -- Initiation: Starting a prescribed regimen -- Implementation: The amount of which a patient's medication-taking behavior corresponds with the treatment regimen or protocol -- Discontinuation: Stopping a perscribed regimen - -For more information see: - -- [A new taxonomy for describing and defining adherence to medications (Vrijens et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC3403197/) -- [Pediatric self-management: A framework for research, practice, and policy. 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zPlXR?P7T;YxyNe&I5m?+(}fp8Pkc4JgGq!n^3dwwv89ty8l*tM+9aS`k%{?jqVp;J z+gLkOcXjQwZONm+qRc|3J^*thk-f>eE9{=B(gRG+vvSGAjD(3Meu5X~>Dx7n6!RZl`xKC4Qej0SdK{9NXTcfm^N}A^q&$a{} zDQtnIHsXk*alKRtO97j50_>?q)zvm>6Oeo>^L!<8u7?F@)B~>+O3eP5xby(63b2dA zQFTiBso*=hI;F77Ja-Sm0FWm1!C9YZ;KfpD<<(wviQagKv&(^H2d{z;5lU?Aov*d5 zlZhu*{Lr}3exSvO4E#Rgd|_AFP=)v4liG>%TAEcbpc_(mq<$+uvtYhe;IbQ+E~}zq zR~@5?Rf1Zpd#yMTQ%^1U{M@3;gY-f3+>Z)>Bv2>&vvMhHD5IbDHS28cYFq+x>~!Zl z)HAB4Kfvc3-(3*j-U386Ax;}A?i$Ds5hK-gy_u~tc`*V`B6?b75*>StSJ?$<(nYJO z`Lpf4L~gXq8sNoXy`bnpcX-;g?^3GX9MqwfTAAUOZpNWT+myEgzDS0=|I01}&FjQgm8r_PGXM1K9k(ym{O-5p4`qJ*rgxup* z7}oqxBsbK@$3fb19Gr`Y4j}fVOKLAdhdZGB?|<%g*h0H$%P~9A{DU}jmP1-AleRRd z9z59K1XO@YD6j)gKB&GVsg*NM{elfH^i{%Ebje!zRrmqgNV;WiruLj5Yl0a3x)ZP# z*KwZcf`bca9O4r@kO2O(`lqkV_se+>BQa=9g}5;zrhGeTxT!x1&LCN*#rXi;>RZ`w z-DC##B0IwLN7Mj_<#LX$o<>lFo+_9KZys8gUx*sK*A#< sJC5ceBcs4lkX;Av8JX+ - -These data can be accessed not only by health professionals, but also by patients through patient portals. EHR data can also be used to enable data-driven interventions such as provider- and patient-facing clinical decision support (CDS) and population health management (PHM). - -CDS has been defined as tools that "provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care" [@Osheroff_2007]. Examples of widely adopted CDS tools with demonstrated effectiveness for cancer prevention, diagnosis, and care include: - - * provider and patient reminders for cancer screening - * decision aids and shared decision-making tools (e.g., for prostate cancer treatment, lung cancer screening) - * AI-supported diagnostic imaging - * chemotherapy decision support - * at-home symptom care - -While CDS tools generally provide decision support focused on a specific patient at a time, PHM are strategies that target specific patient populations [@Swarthout_Bishop_2017]. PHM efforts generally consist of: - - * population algorithms that are applied over EHR and other data sources to identify individuals who are eligible for a specific healthcare intervention (e.g., colorectal cancer screening, tobacco cessation, HPV vaccination) - * patient engagement strategies (e.g., patient portals, text messaging, chatbots, patient navigators) that provide education and access to those interventions - * analytic tools that assess the effectiveness of the PHM program. - -Several PHM programs have demonstrated to be effective in increasing the uptake of cancer prevention. For example, the colorectal cancer screening program at Kaiser Permanente uses digital (i.e., text messaging, patient portal), mailed, and patient navigation approaches to increase colorectal cancer screening by mailing Fecal Immunohistochemical Test (FIT) kits to patients' homes [@Gupta2020]. Also the Cancer Moonshot BRIDGE trial used the GARDE platform [@Bradshaw2022] (ITCR-funded) to identify candidates for genetic testing of hereditary cancer syndromes based on EHR data; and for patient outreach, pre- and post-test education via automated chatbots [@Kaphingst2024]. - - -Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. - -Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. - -While some CDS and PHM approaches have been successfully adopted widely, emerging technologies such as the use of generative AI approaches to analyze diagnostic imaging, large language models (LLMs) to extract information from narrative texts (e.g., clinical notes), LLM-based chatbots to communicate with patients, and digital health tools such as home-based sensors are creating unprecedented opportunities for next generation CDS and PHM. These approaches have the potential to enable significant breakthroughs through the implementation of patient-tailored cancer prevention and care at a population scale. Nevertheless, substantial research is needed to ensure effective and fair implementation of these CDS and PHM interventions. - - - -## Types of questions that can be asked with clinical data - -### Risk Prediction - -Risk prediction in clinical research involves using data to assess the likelihood of certain outcomes or events occurring in patients. This could include predicting the risk of developing a particular disease, experiencing a specific complication, or responding to a treatment. - -Data used for risk prediction can come from various sources, including: - - * Clinical Data: This includes patient demographics, medical history, laboratory results, and imaging studies. - * Genetic Data: Genetic information, such as DNA sequencing results, can provide valuable insights into an individual's susceptibility to certain diseases. - * Environmental and Lifestyle Data: Factors such as diet, exercise habits, smoking status, and environmental exposures can influence disease risk and may be included in risk prediction models. - * Biomarkers: Biological markers indicative of disease or physiological processes can be used as predictors in risk models [@Bodaghi_Fattahi_Ramazani_2023]. - -Once relevant data is collected, statistical and machine learning techniques can be applied to develop predictive models. These models aim to identify patterns and relationships within the data that are associated with the outcome of interest. Common techniques include logistic regression, decision trees, random forests, support vector machines, and neural networks. - -After the model is trained on a dataset, it can be validated using independent datasets to assess its performance and generalizability. Once validated, the model can be used to predict risk in new patients based on their individual characteristics and data. - -Clinical prediction rules are a subset of risk prediction models, specific to clinical research. - -Examples of risk prediction models ... - -Risk prediction models are important because ... - -Overall, risk prediction in clinical research allows healthcare professionals to identify individuals at higher risk of certain outcomes, enabling targeted interventions, personalized treatments, and more efficient resource allocation. - -### Cohort identification for research - -Clinical data plays a crucial role in cohort identification for research purposes. Researchers typically use electronic health records (EHRs), medical databases, or registries to identify cohorts based on specific criteria such as age, gender, medical conditions, treatments, medications, and outcomes. Advanced data mining and natural language processing techniques can also be employed to extract relevant information from unstructured data sources like clinical notes. Once cohorts are identified, researchers can analyze the data to study disease progression, treatment effectiveness, and outcomes. - -Cohort identification is important regardless of research study type, but to provide specific examples: - -* Research Design: Identifying cohorts allows researchers to design studies with appropriate inclusion or exclusion criteria. By selecting specific groups of individuals with similar characteristics or exposures, researchers can investigate hypotheses effectively. -* Clinical Insights: Cohort studies enable researchers to observe the natural history of diseases, track outcomes over time, and assess the effectiveness of interventions or treatments. Understanding how different factors influence disease progression or treatment response can inform clinical decision-making and improve patient care. -* Epidemiological Studies: Cohort identification is crucial for epidemiological research to understand the incidence, prevalence, and risk factors associated with diseases. By following cohorts over time, researchers can identify trends, patterns, and associations that contribute to our understanding of disease causation and prevention. -* Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. -* Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. - -### Case report forms (CRFs) - -In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter -- specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). - -Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on **accessibility, sensitivity, specificity, and comfort for people** - -::: {.notice} - -While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren't necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions -- the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. - -::: - -Here, we explore the different categories of questions that may be included within case report forms: - -1. **Demographic and Socioeconomic Questions** - -Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. - -- **Sex and Gender**: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. -- **Ethnicity and Race**: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. -- **Socioeconomic Status**: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, "What is your current employment status?" with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. - -It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. - -2. **Health and Medial History Questions** - -Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. - -- **Medical Conditions and History**: Questions about past and present health conditions should use clear, accessible language. For example, "Have you ever been diagnosed with any of the following conditions? (Please check all that apply)" followed by a comprehensive list containing all necessary options. -- **Medication Use**: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. -- **Disability and Functional Status**: For many populations, it is important to use person-first language, such as "Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?" and provide space for participants to describe their specific needs. However, different populations have different preferences. The "Disability Language Style Guide" from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic [@ncdj_style_guide]. - -Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. - -3. **Experience and Quality of Life Questions** - -Understanding how health conditions and treatments affect participants' daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. - -- **Daily Living and Social Functioning**: Questions like "How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?" can help assess the impact on daily life, with options ranging from "never" to "always." -- **Emotional and Psychological Well-Being**: Including questions such as "In the past week, how often have you felt anxious or depressed?" using a scale from "not at all" to "very often" can provide insights into mental health needs. -- **Support Systems and Social Networks**: Asking about social support (e.g., "Do you have someone you can rely on for emotional support?") can help identify participants' needs for social and emotional resources. - -Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. - -4. **Sexual and Reproductive Health Questions** - -Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. - -- **Sexual Orientation and Gender Identity (SOGI)**: Instead of just predefined categories, open-ended questions like "How would you describe your sexual orientation?" and "What is your gender identity?" allow participants to self-identify. Offering the option to skip these questions respects participants' privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. -- **Reproductive Health**: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, "Are you currently using any form of contraception? If yes, please specify." -- **Sexual Activity and History**: Questions should be direct but framed sensitively, such as "Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs." - -These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. - -5. **Treatment Preferences and Decision-Making Questions** - -Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. - -- **Decision-Making Preferences**: Questions like "How involved would you like to be in decisions about your healthcare?" offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. -- **Cultural and Religious Considerations**: Asking, "Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?" ensures that care is respectful and culturally appropriate. -- **Treatment Burden**: Questions such as "What level of inconvenience or side effects would be acceptable to you when considering a treatment?" help to gauge participants' preferences and comfort levels. - -These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. - -6. **Accessibility and Accommodation Needs Questions** - -To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. - -- **Language and Communication Needs**: "What is your preferred language for communication? Do you need an interpreter or translated materials?" These questions help ensure that participants can understand the materials. -- **Physical Accessibility**: Asking, "Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?" ensures physical accessibility. -- **Format Preferences**: "Would you prefer to complete this form online, on paper, or verbally with assistance?" helps accommodate different needs and preferences. -Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. - -Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. - -7. **Cultural Sensitivity and Identity Questions** - -CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. - -- **Cultural Identity and Practices**: An open-ended question such as "Are there any cultural practices or beliefs that are important for us to be aware of in your care?" allows participants to share relevant information. -- **Dietary Restrictions and Preferences**: Asking, "Do you have any dietary restrictions or preferences that are culturally or religiously motivated?" ensures that these are respected. -- **Community and Belonging**: "Is there anything about your community or background that you would like us to know to provide better care?" encourages participants to share relevant aspects of their identity. - -These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. - -8. **Ensuring Comfort, Trust, and Privacy Questions** - -Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. - -- **Comfort and Confidentiality**: Asking, "Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?" helps build trust. -- **Feedback and Preferences**: "Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?" invites participants to share their feedback. -- **Consent and Voluntary Participation**: Questions like, "Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary." reinforce autonomy and respect. - -Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. - -::: {.warning} - -The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. - -::: - -Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. By thoughtfully addressing both unique study requirements and universally relevant data points, researchers can optimize CRFs for consistency across clinical studies. - - -### Clinical studies and trials - -Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. - -Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. - -::: {.definition} -The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." -::: - -The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. - -::: {.definition} -**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. -::: - -Here, we explore the different categories of questions that can be addressed using clinical trials data, ranging from the evaluation of treatment efficacy and safety to the exploration of predictive factors for disease prognosis and the personalization of medical care. - -1. **Efficacy and Effectiveness Questions** - -One of the primary objectives of clinical trials is to determine the **efficacy of new treatments or interventions**. Clinical data is used to answer fundamental questions such as "Is the new treatment effective?" and "How does its effectiveness compare to existing treatments?" - -Researchers design studies to measure the **magnitude of treatment effects**, comparing outcomes between treatment and control groups (e.g., a new drug versus a placebo or standard care). - -Beyond controlled settings, data from real-world evidence (RWE) studies can further address questions about the effectiveness of treatments when applied in routine clinical practice. For example, "Is Drug A more effective than Drug B in managing hypertension in a real-world patient population?" Such questions help to assess how treatments perform outside the controlled environment of clinical trials where patient adherence comorbidities, and polypharmacy may influence outcomes. While clinical trials are an important source of real-world data (RWD), they are typically conducted under tightly controlled conditions, which may not fully reflect the complexities of real-world studies. This makes RWE particularly valuable for understanding how treatments work when patients have varying levels of adherence or multiple chronic conditions that are often excluded from clinical trails. - -::: {.definition} -**Comorbidities**: refer to the presence of one or more additional medical conditions or diseases that coexist with a primary condition in a patient. These conditions can occur independently or be related to the primary disease. -::: - -2. **Safety and Tolerability Questions** - -Safety is a critical aspect of any clinical study, and clinical data is essential in identifying and characterizing the safety profile of new treatments. Questions like "What are the common adverse effects of the new treatment?" or "What is the incidence of serious adverse events (SAEs) in patients receiving Drug A?" are addressed by carefully monitoring and recording adverse events throughout the study. Comparative safety questions also arise, such as "Does Drug A have fewer adverse effects compared to Drug B?" These questions are crucial in weighing the benefits of a treatment against its risk and in identifying specific populations that may be at higher risk of adverse reactions. - -::: {.definition} -**Serious Adverse Events (SAEs)**: Any medical occurrence during a clinical trial or medical treatment that results in significant negative outcomes, such as death, life-threatening situations, hospitalization (or its prolongation), persistent or significant disability, or congenital anomalies. SAEs also include any event that requires intervention to prevent one of these outcomes. -::: - -::: {.definition} -**Incidence**: Refers to the rate or number of new cases of SAEs that occur within a specific population during a defined time period, typically during a clinical trial or study. Is it usually expressed as a proportion, such as the number of SAEs per 100 or 1,000 participants, and helps measure how frequently these serious events happen among those exposed to a particular treatment or intervention. -::: - -3. **Comparative Effectiveness and Cost-Effectiveness Questions** - -Beyond safety and efficacy, clinical data can be used to evaluate the comparative effectiveness of different treatments and their cost-effectiveness. These questions often guide healthcare policy and clinical guidelines: "Is Drug A more cost-effective than standard care for managing chronic heart failure?" and "What is the cost per quality-adjusted life year (QALY) gained with the new intervention?" Such analyses provide valuable information for stakeholders, including healthcare providers, payers, and policymakers, to make informed decisions on resource allocation. - -::: {.definition} -**Cost per quality-adjusted life year (QALY)** is a metric used in health economics to assess the value of a medical intervention by measuring the cost of gaining one year of life adjusted for its quality. A QALY incorporates both the **quantity** and **quality** of life lived, where one QALY is equivalent to one year of life in perfect health. If a treatment improves both the length and quality of life, it earns more QALYs. The **cost per QALY** is calculated by dividing the cost of the treatment by the number of QALYs gained, helping to determine the cost-effectiveness of healthcare interventions. Lower cost per QALY indicates better value for money in terms of health benefits achieved [-@Drummond_Sculpher_Claxton_Stoddart_Torrance_2015]. -::: - -4. **Mechanistic and Biomarker Questions** - -Clinical studies often explore not just whether a treatment works, but also how it works. Mechanistic questions delve into the underlying biological pathways affected by a treatment. For instance, "Does Drug A work by inhibiting a specific enzyme involved in the disease process?" Biomarker data can play a key role in such analyses, helping to identify molecular markers that predict response to treatment. Questions like "Are there biomarkers that can predict which patients are more likely to benefit from Drug A?" help in developing targeted therapies and personalized treatment approaches. - -5. **Predictive and Prognostic Questions** - -Clinical trials and studies frequently aim to identify factors that predict disease outcomes or treatment responses. For example, "What baseline characteristics predict a better response to Drug A?" or "What are the predictors of mortality in patients with severe heart failure?" Such prognostic and predictive questions are crucial for identifying high-risk patients, guiding treatment decisions, and developing clinical guidelines. Identifying patient subgroups that benefit more or less from a treatment also facilitates personalized medicine approaches, ensuring that the right treatment is delivered to the right patient at the right time. - -With the advent of personalized medicine, clinical data is increasingly being used to answer questions tailored to individual patient profiles. For instance, "What patient characteristics modify the effect of the treatment?" or "Can we predict which patients are most likely to benefit from Drug A using machine learning models?" These questions are at the forefront of precision medicine, where the goal is to customize healthcare to each patient based on their unique genetic, biomarker, and clinical characteristics. - -6. **Quality of Life and Patient-Reported Outcome Questions** - -In modern clinical research, the impact of treatments on patients' quality of life and their subjective experiences has gained prominence. Questions such as "How does Drug A affect patients' quality of life compared to standard care?" or "What are the patient-reported outcomes associated with the new intervention?" are increasingly addressed in clinical trials. Data from quality of life assessments which are standardized and validated questionnaires (for example, the Health Assessment Questionnaire or HAQ), patient-reported outcome measures (PROMs, tools or questionnaires used to assess a patient's health status or health-related quality of life directly from the patient's perspective), and other patient-centric endpoints (Smoking History, Surgical History, etc.) provide valuable insights into how treatments affect patients beyond traditional clinical endpoints. - -7. **Longitudinal and Follow-Up Questions** - -Long-term follow-up studies are essential for understanding the durability of treatment effects and any delayed adverse effects. Questions like "What are the long-term outcomes associated with Drug A?" and "Is there a sustained benefit of Drug A in reducing symptoms after five years?" are vital for assessing the overall value of treatments. Such longitudinal analyses help in determining the optimal duration of therapy, the need for maintenance treatments, and the overall benefit-risk profile of an intervention. - -8. **Safety in Special Populations Questions** - -Clinical data can also be used to evaluate the safety and efficacy of treatments in special populations, such as children, pregnant women, or elderly patients. Questions such as "Is Drug A safe for use in pregnant women?" and "What is the risk of adverse events when Drug A is administered to patients with multiple comorbidities?" are essential for developing age- and condition-specific clinical guidelines. These questions help ensure that treatments are tailored to the needs of different patient populations, minimizing harm and maximizing benefits. - -9. **Adherence and Compliance Questions** - -Patient adherence to prescribed treatment regimens can significantly impact the outcomes of clinical studies. Questions like "What factors influence patient adherence to the new treatment?" and "How does adherence affect treatment efficacy?" are addressed through analyzing adherence data through monitors if a regulated study, or with interim/baseline analysis if non-regulated. Understanding these factors can lead to interventions that improve adherence and, consequently, the effectiveness of the treatment. - -10. **Real-World Evidence and Generalizability Questions** - -Real-world evidence (RWE) studies help bridge the gap between clinical trial results and clinical practice. Questions such as "How generalizable are the results of this clinical trial to the broader population?" or "What is the impact of real-world use patterns on treatment outcomes?" are essential for understanding how well clinical trial findings apply to everyday clinical settings. These questions help determine if the benefits observed in clinical trials can be replicated in diverse patient populations under routine care conditions. - -11. **Prevention and Risk Reduction Questions** - -Clinical data is also crucial in answering questions related to prevention and risk reduction strategies. For example, "Can the new treatment reduce the risk of developing diabetes in high-risk individuals?" or "What factors are associated with a reduced risk of cardiovascular disease in a large cohort study?" These questions are fundamental in preventive medicine, guiding public health interventions and informing clinical practice. - - -Clinical Data provides a wealth of information that can be leveraged to answer a broad array of questions in clinical trials and studies. From understanding treatment efficacy and safety to exploring long-term outcomes, quality of life impacts, and the potential for personalized medicine, clinical data is foundational to advancing medical knowledge and improving patient care. Case report forms (CRFs) are instrumental in identifying patient cohorts or subgroups, documenting baseline characteristics, and capturing information on comorbidities, while also monitoring adherence data, which are essential for ensuring accurate and meaningful analysis. The type of questions that can be asked and answered are continually evolving as new data sources, analytical methods, and research paradigms emerge, further enriching the field of clinical research. - - -### Retrospective analysis - -Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. - -Here, we explore the different categories of questions that can be addressed using retrospective data analysis: - -1. **Descriptive Questions** - -Descriptive questions aim to summarize and describe the characteristics of a patient population, disease, or healthcare process. They provide a foundational understanding of a dataset and are often the starting point for more complex analyses. - -- **Patient Demographics and Characteristics**: What are the demographic profiles (age, gender, ethnicity, socioeconomic status) of patients diagnosed with a specific condition? What are the common comorbidities and risk factors in this population? -- **Disease Prevalence and Incidence**: What is the prevalence or incidence of a specific disease or condition in a particular population or geographic area over a defined period? -- **Clinical Presentation**: What are the most common presenting symptoms, clinical signs, or laboratory findings associated with a specific disease or condition? - -These questions help in understanding the baseline characteristics and epidemiology of diseases, which is crucial for planning further studies or developing public health interventions. - -2. **Treatment and Intervention Questions** - -Retrospective data can be used to examine the real-world effectiveness, safety, and patterns of treatment and interventions. These questions are critical for understanding how treatments are applied in clinical practice and their outcomes: - -- **Treatment Effectiveness**: What is the effectiveness of a specific treatment or intervention in reducing symptoms, improving quality of life, or achieving clinical outcomes in a real-world setting? -- **Adherence and Persistence**: What are the rates of adherence and persistence with a prescribed treatment regimen over time? What factors are associated with higher or lower adherence rates? -- **Comparative Effectiveness**: How does the effectiveness of one treatment compare to another in a similar population? What are the relative benefits and risks associated with different treatment options? - -By examining historical data, researchers can gain insights into how different treatments perform outside the controlled environment of clinical trials, thereby informing clinical decision-making and guidelines. - -3. **Outcome and Prognostic Questions** - -Understanding patient outcomes and prognostic factors is central to retrospective analyses. These questions focus on the end results of healthcare practices and patient management, including survival, complications, and quality of life: - -- **Survival and Mortality**: What are the survival rates and mortality rates associated with a particular disease, condition, or treatment? What factors are associated with increased or decreased survival rates? -- **Complication Rates**: What are the rates and types of complications associated with specific diseases, surgeries, or treatments? Are there identifiable risk factors for these complications? -- **Prognostic Factors**: What are the key prognostic factors (e.g., age, stage of disease, comorbidities) that influence the outcomes of patients with a particular condition? - -These questions help identify factors that influence patient outcomes, guiding clinicians in predicting disease progression and tailoring individual treatment plans. - -4. Risk Factor and Predictive Modeling Questions - -Retrospective analyses are often used to identify potential risk factors for diseases and develop predictive models. These questions aim to uncover associations between variables and outcomes to help predict future events: - -- **Risk Factors for Disease**: What are the risk factors associated with developing a particular disease or condition? Are there demographic, genetic, behavioral, or environmental factors that significantly increase the risk? -- **Prediction Models**: Can we develop a predictive model to estimate the likelihood of disease progression, relapse, or adverse outcomes based on historical patient data? -- **Multivariable Analysis**: How do multiple factors interact to influence the risk of an outcome? Are there synergistic or antagonistic effects between different risk factors? - -These questions are critical for developing tools that help clinicians assess risk and make more informed decisions in preventive care and early intervention. - -5. **Healthcare Utilization and Cost Questions** - -Understanding how healthcare resources are utilized and the associated costs is important for optimizing healthcare delivery. Retrospective data can provide valuable insights into patterns of care and resource allocation for questions such as: - -- **Healthcare Resource Utilization**: What are the patterns of healthcare utilization (e.g., hospitalizations, emergency room visits, outpatient visits) for patients with a specific condition? How does utilization vary by patient demographics, disease severity, or geographic location? -- **Cost of Care**: What are the direct and indirect costs associated with managing a specific disease or condition? How do different treatment options impact overall healthcare costs? -- **Cost-Effectiveness**: How does the cost-effectiveness of one treatment or intervention compare to another? What are the economic impacts of different clinical management strategies? - -These questions can inform healthcare policy, budget planning, and the development of strategies to improve cost-effectiveness and access to care. - -6. **Quality of Care and Patient Safety Questions** - -Retrospective analysis can be used to evaluate the quality and safety of care provided to patients, identifying areas for improvement and potential patient safety concerns, helping to answer questions such as: - -- **Quality Indicators**: Quality indicators are standardized measures of health care quality and may focus on more specialized groups of care like preventative, inpatient or emergency care, pediatric or maternal health, etc. What are the levels of observed mortality or morbidity for a certain population in a specific setting? How utilized are certain procedures? [@AHRQ] -- **Adverse Events and Safety**: What is the incidence of adverse events, medication errors, or healthcare-associated infections in a particular patient population? What factors are associated with higher or lower rates of adverse events? -- **Patient Satisfaction and Experience**: What are the reported levels of patient satisfaction with healthcare services? How does satisfaction vary by demographic or clinical characteristics? - -These questions help in assessing healthcare quality, identifying gaps in care, and developing interventions to enhance patient safety and satisfaction. - -7. **Temporal and Trend Analysis Questions** - -Retrospective studies often involve analyzing temporal patterns to understand how diseases, treatments, or outcomes change over time. These questions help in recognizing trends and patterns that can inform future research and policy decisions: - -- **Temporal Trends in Disease Incidence and Prevalence**: How have the incidence and prevalence of a specific disease changed over the past decade? Are there seasonal or cyclical patterns in disease occurrence? -- **Trends in Treatment Practices**: How have treatment practices and prescribing patterns evolved over time? Are there emerging trends in the use of certain medications or interventions? -- **Trends in Outcomes and Survival**: How have patient outcomes, such as survival rates or complication rates, changed over time with advances in treatment and healthcare delivery? - -These questions are essential for monitoring public health trends, evaluating the impact of interventions, and planning future healthcare strategies. - -8. **Subgroup and Stratified Analysis Questions** - -Subgroup analyses can help uncover differences in disease characteristics, treatment responses, and outcomes among different patient groups. This type of analysis is particularly valuable for personalized medicine and addressing questions related to health disparities: - -- **Differences by Demographic Group**: How do treatment outcomes, risk factors, or disease characteristics differ between various demographic groups (e.g., age, gender, race/ethnicity)? -- **Subgroup-Specific Effectiveness**: Does a particular treatment work better in one subgroup of patients compared to another? What factors might explain these differences? -- **Health Disparities**: Are there disparities in healthcare access, treatment outcomes, or disease burden across different socioeconomic or minority groups? - -Questions focusing on subgroups are crucial for understanding variations in care and outcomes, identifying at-risk populations, and developing targeted interventions. - - -Retrospective analysis of clinical data allows researchers to ask a wide range of questions that provide valuable insights into patient characteristics, disease epidemiology, treatment effectiveness, outcomes, risk factors, healthcare utilization, and more. By carefully formulating questions and analyzing historical data, researchers can uncover patterns, identify trends, and generate evidence that informs clinical practice, healthcare policy, and future research directions. This chapter outlines the various categories of questions that can be addressed through retrospective analysis, highlighting the potential of this approach to advance knowledge and improve patient care in diverse clinical settings. - - -## Research Design Considerations - -Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. - -One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, - -* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. -* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. -* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. -* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. - -Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: - -* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. -* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. -* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. -* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. - -With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. - 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- - - - - - - - - - - - - diff --git a/docs/no_toc/About.md b/docs/no_toc/About.md deleted file mode 100644 index 6d2c2c9..0000000 --- a/docs/no_toc/About.md +++ /dev/null @@ -1,143 +0,0 @@ ---- -title: "Clinical Data Analysis for Cancer Informatics" ---- - -# About the Authors {-} - -These credits are based on our [course contributors table guidelines](https://github.com/jhudsl/DaSL_Course_Template_Bookdown/wiki/How-to-give-credits). - -  -  - -|Credits|Names| -|-------|-----| -|**Pedagogy**|| -|Lead Content Instructor(s)|[FirstName LastName](link to personal website)| -|Lecturer(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involved| Delivered the course in some way - video or audio| -|Content Author(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involved | If any other authors besides lead instructor| -|Content Contributor(s) (include section name/link in parentheses) - make new line if more than one section involved| Jennifer Kelleher, Ph.D.1*; Abigail S. Robbertz, Ph.D.| -|Content Editor(s)/Reviewer(s) | Julia K. Herriott, B.S.| -|Content Director(s) | Helped guide the content direction| -|Content Consultants (include chapter name/link in parentheses or word "General") - make new line if more than one chapter involved | Gave high level advice on content| -|Acknowledgments| Gave small assistance to content but not to the level of consulting | -|**Production**|| -|Content Publisher(s)| Helped with publishing platform| -|Content Publishing Reviewer(s)| Reviewed overall content and aesthetics on publishing platform| -|**Technical**|| -|Course Publishing Engineer(s)| Helped with the code for the technical aspects related to the specific course generation| -|Template Publishing Engineers|[Candace Savonen](https://www.cansavvy.com/), [Carrie Wright](https://carriewright11.github.io/)| -|Publishing Maintenance Engineer|[Candace Savonen](https://www.cansavvy.com/)| -|Technical Publishing Stylists|[Carrie Wright](https://carriewright11.github.io/), [Candace Savonen](https://www.cansavvy.com/)| -|Package Developers ([Leanbuild](https://github.com/jhudsl/leanbuild))|[John Muschelli](https://johnmuschelli.com/), [Candace Savonen](https://www.cansavvy.com/), [Carrie Wright](https://carriewright11.github.io/)| -|**Art and Design**|| -|Illustrator(s)| Created graphics for the course| -|Figure Artist(s)| Created figures/plots for course| -|Videographer(s)| Filmed videos| -|Videography Editor(s)| Edited film| -|Audiographer(s)| Recorded audio| -|Audiography Editor(s)| Edited audio recordings| -|**Funding**|| -|Funder(s)| Institution/individual who funded course including grant number| -|Funding Staff| Staff members who help with funding| - -  - - -``` -## ─ Session info ─────────────────────────────────────────────────────────────── -## setting value -## version R version 4.3.2 (2023-10-31) -## os Ubuntu 22.04.4 LTS -## system x86_64, linux-gnu -## ui X11 -## language (EN) -## collate en_US.UTF-8 -## ctype en_US.UTF-8 -## tz Etc/UTC -## date 2026-05-10 -## pandoc 3.1.1 @ /usr/local/bin/ (via rmarkdown) -## -## ─ Packages ─────────────────────────────────────────────────────────────────── -## package * version date (UTC) lib source -## bookdown 0.46 2025-12-05 [1] CRAN (R 4.3.2) -## bslib 0.6.1 2023-11-28 [1] RSPM (R 4.3.0) -## cachem 1.0.8 2023-05-01 [1] RSPM (R 4.3.0) -## chromote 0.5.1 2025-04-24 [1] CRAN (R 4.3.2) -## cli 3.6.5 2025-04-23 [1] CRAN (R 4.3.2) -## devtools 2.4.5 2022-10-11 [1] RSPM (R 4.3.0) -## digest 0.6.34 2024-01-11 [1] RSPM (R 4.3.0) -## dplyr 1.1.4 2023-11-17 [1] RSPM (R 4.3.0) -## ellipsis 0.3.2 2021-04-29 [1] RSPM (R 4.3.0) -## evaluate 1.0.5 2025-08-27 [1] CRAN (R 4.3.2) -## fansi 1.0.6 2023-12-08 [1] RSPM (R 4.3.0) -## fastmap 1.1.1 2023-02-24 [1] RSPM (R 4.3.0) -## fs 1.6.3 2023-07-20 [1] RSPM (R 4.3.0) -## generics 0.1.3 2022-07-05 [1] RSPM (R 4.3.0) -## gitcreds 0.1.2 2022-09-08 [1] RSPM (R 4.3.0) -## glue 1.7.0 2024-01-09 [1] RSPM (R 4.3.0) -## hms 1.1.3 2023-03-21 [1] RSPM (R 4.3.0) -## htmltools 0.5.7 2023-11-03 [1] RSPM (R 4.3.0) -## htmlwidgets 1.6.4 2023-12-06 [1] RSPM (R 4.3.0) -## httpuv 1.6.14 2024-01-26 [1] RSPM (R 4.3.0) -## httr 1.4.7 2023-08-15 [1] RSPM (R 4.3.0) -## jquerylib 0.1.4 2021-04-26 [1] RSPM (R 4.3.0) -## jsonlite 2.0.0 2025-03-27 [1] CRAN (R 4.3.2) -## knitr 1.50 2025-03-16 [1] CRAN (R 4.3.2) -## later 1.3.2 2023-12-06 [1] RSPM (R 4.3.0) -## lifecycle 1.0.4 2023-11-07 [1] RSPM (R 4.3.0) -## magrittr 2.0.3 2022-03-30 [1] RSPM (R 4.3.0) -## memoise 2.0.1 2021-11-26 [1] RSPM (R 4.3.0) -## mime 0.12 2021-09-28 [1] RSPM (R 4.3.0) -## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.3.0) -## ottrpal 2.0.0 2025-12-16 [1] Github (ottrproject/ottrpal@6d1267e) -## pillar 1.9.0 2023-03-22 [1] RSPM (R 4.3.0) -## pkgbuild 1.4.3 2023-12-10 [1] RSPM (R 4.3.0) -## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.3.0) -## pkgload 1.4.1 2025-09-23 [1] CRAN (R 4.3.2) -## processx 3.8.6 2025-02-21 [1] CRAN (R 4.3.2) -## profvis 0.3.8 2023-05-02 [1] RSPM (R 4.3.0) -## promises 1.2.1 2023-08-10 [1] RSPM (R 4.3.0) -## ps 1.9.1 2025-04-12 [1] CRAN (R 4.3.2) -## purrr 1.0.2 2023-08-10 [1] RSPM (R 4.3.0) -## R6 2.6.1 2025-02-15 [1] CRAN (R 4.3.2) -## Rcpp 1.0.12 2024-01-09 [1] RSPM (R 4.3.0) -## readr 2.1.5 2024-01-10 [1] RSPM (R 4.3.0) -## remotes 2.4.2.1 2023-07-18 [1] RSPM (R 4.3.0) -## rlang 1.1.6 2025-04-11 [1] CRAN (R 4.3.2) -## rmarkdown 2.25 2023-09-18 [1] RSPM (R 4.3.0) -## rprojroot 2.1.1 2025-08-26 [1] CRAN (R 4.3.2) -## rvest 1.0.5 2025-08-29 [1] CRAN (R 4.3.2) -## sass 0.4.8 2023-12-06 [1] RSPM (R 4.3.0) -## sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.3.0) -## shiny 1.8.0 2023-11-17 [1] RSPM (R 4.3.0) -## spelling 2.3.2 2025-08-18 [1] CRAN (R 4.3.2) -## stringi 1.8.3 2023-12-11 [1] RSPM (R 4.3.0) -## stringr 1.5.1 2023-11-14 [1] RSPM (R 4.3.0) -## tibble 3.3.0 2025-06-08 [1] CRAN (R 4.3.2) -## tidyr 1.3.1 2024-01-24 [1] RSPM (R 4.3.0) -## tidyselect 1.2.0 2022-10-10 [1] RSPM (R 4.3.0) -## tzdb 0.4.0 2023-05-12 [1] RSPM (R 4.3.0) -## urlchecker 1.0.1 2021-11-30 [1] RSPM (R 4.3.0) -## usethis 2.2.3 2024-02-19 [1] RSPM (R 4.3.0) -## utf8 1.2.4 2023-10-22 [1] RSPM (R 4.3.0) -## vctrs 0.6.5 2023-12-01 [1] RSPM (R 4.3.0) -## webshot2 0.1.2 2025-04-23 [1] CRAN (R 4.3.2) -## websocket 1.4.4 2025-04-10 [1] CRAN (R 4.3.2) -## xfun 0.55 2025-12-16 [1] CRAN (R 4.3.2) -## xml2 1.5.1 2025-12-01 [1] CRAN (R 4.3.2) -## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.3.0) -## yaml 2.3.12 2025-12-10 [1] CRAN (R 4.3.2) -## -## [1] /usr/local/lib/R/site-library -## [2] /usr/local/lib/R/library -## -## ────────────────────────────────────────────────────────────────────────────── -``` - - diff --git a/docs/no_toc/References.md b/docs/no_toc/References.md deleted file mode 100644 index 48143a7..0000000 --- a/docs/no_toc/References.md +++ /dev/null @@ -1,3 +0,0 @@ - -# References {-} - diff --git a/docs/no_toc/_updated_output.yml b/docs/no_toc/_updated_output.yml deleted file mode 100644 index e1e21cc..0000000 --- a/docs/no_toc/_updated_output.yml +++ /dev/null @@ -1,18 +0,0 @@ -bookdown::gitbook: - css: assets/style_ITN.css - includes: - before_body: assets/big-image-itcr.html - after_body: assets/footer.html - in_header: /__w/Clinical_Data_Management/Clinical_Data_Management/docs/no_toc/assets/links.html - highlight: tango - config: - toc: - collapse: section - before: | - - after: |- -

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Style adapted from: rstudio4edu-book (CC-BY 2.0)

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Click here to provide feedback

diff --git a/docs/no_toc/about-the-authors.html b/docs/no_toc/about-the-authors.html deleted file mode 100644 index 8a9f56c..0000000 --- a/docs/no_toc/about-the-authors.html +++ /dev/null @@ -1,513 +0,0 @@ - - - - - - - About the Authors | Clinical Data Analysis for Cancer Informatics - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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About the Authors

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- ---- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
CreditsNames
Pedagogy
Lead Content Instructor(s)FirstName LastName
Lecturer(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involvedDelivered the course in some way - video or audio
Content Author(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involvedIf any other authors besides lead instructor
Content Contributor(s) (include section name/link in parentheses) - make new line if more than one section involvedJennifer Kelleher, Ph.D.1*; Abigail S. Robbertz, Ph.D.
Content Editor(s)/Reviewer(s)Julia K. Herriott, B.S.
Content Director(s)Helped guide the content direction
Content Consultants (include chapter name/link in parentheses or word “General”) - make new line if more than one chapter involvedGave high level advice on content
AcknowledgmentsGave small assistance to content but not to the level of consulting
Production
Content Publisher(s)Helped with publishing platform
Content Publishing Reviewer(s)Reviewed overall content and aesthetics on publishing platform
Technical
Course Publishing Engineer(s)Helped with the code for the technical aspects related to the specific course generation
Template Publishing EngineersCandace Savonen, Carrie Wright
Publishing Maintenance EngineerCandace Savonen
Technical Publishing StylistsCarrie Wright, Candace Savonen
Package Developers (Leanbuild)John Muschelli, Candace Savonen, Carrie Wright
Art and Design
Illustrator(s)Created graphics for the course
Figure Artist(s)Created figures/plots for course
Videographer(s)Filmed videos
Videography Editor(s)Edited film
Audiographer(s)Recorded audio
Audiography Editor(s)Edited audio recordings
Funding
Funder(s)Institution/individual who funded course including grant number
Funding StaffStaff members who help with funding
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## ─ Session info ───────────────────────────────────────────────────────────────
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-##  system   x86_64, linux-gnu
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-##  tz       Etc/UTC
-##  date     2026-05-10
-##  pandoc   3.1.1 @ /usr/local/bin/ (via rmarkdown)
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Chapter 6 Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards

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6.1 SNOMED

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6.2 ICD10

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6.3 RxNorm

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6.4 LOINC

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6.5 Emerging standards such as FHIR

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6.6 OMOP

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-
-
- All illustrations CC-BY. -
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-
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OR this works:

- -
- - -
-

2.1.6 Citation examples

-

We can put citations at the end of a sentence like this (Allaire et al. 2021). -Or multiple citations Xie, Allaire, and Grolemund (2018).

-

but they need a ; separator (Allaire et al. 2021; Xie, Allaire, and Grolemund 2018).

-

In text, we can put citations like this Allaire et al. (2021).

-
-
- - -

References

-
-
-Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2021. Rmarkdown: Dynamic Documents for r. https://github.com/rstudio/rmarkdown. -
-
-Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown. -
-
-
-
-
- All illustrations CC-BY. -
- All other materials CC-BY unless noted otherwise. -
-
- - - - - - - - - - - - - - - - - - - - - - diff --git a/packages.bib b/packages.bib index b501b2a..3e51185 100644 --- a/packages.bib +++ b/packages.bib @@ -3,32 +3,32 @@ @Manual{R-base author = {{R Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, - year = {2020}, + year = {2025}, url = {https://www.R-project.org/}, } @Manual{R-bookdown, title = {bookdown: Authoring Books and Technical Documents with R Markdown}, author = {Yihui Xie}, - year = {2021}, - note = {https://github.com/rstudio/bookdown, -https://pkgs.rstudio.com/bookdown/}, + year = {2025}, + note = {R package version 0.46}, + url = {https://github.com/rstudio/bookdown}, } @Manual{R-knitr, title = {knitr: A General-Purpose Package for Dynamic Report Generation in R}, author = {Yihui Xie}, - year = {2021}, - note = {R package version 1.33}, + year = {2025}, + note = {R package version 1.51}, url = {https://yihui.org/knitr/}, } @Manual{R-rmarkdown, title = {rmarkdown: Dynamic Documents for R}, - author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone}, - year = {2021}, - note = {https://github.com/rstudio/rmarkdown, -https://pkgs.rstudio.com/rmarkdown/}, + author = {JJ Allaire and Yihui Xie and Christophe Dervieux and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone}, + year = {2026}, + note = {R package version 2.31}, + url = {https://github.com/rstudio/rmarkdown}, } @Book{bookdown2016, @@ -37,7 +37,7 @@ @Book{bookdown2016 publisher = {Chapman and Hall/CRC}, address = {Boca Raton, Florida}, year = {2016}, - note = {ISBN 978-1138700109}, + isbn = {978-1138700109}, url = {https://bookdown.org/yihui/bookdown}, } @@ -60,7 +60,6 @@ @InCollection{knitr2014 publisher = {Chapman and Hall/CRC}, year = {2014}, note = {ISBN 978-1466561595}, - url = {http://www.crcpress.com/product/isbn/9781466561595}, } @Book{rmarkdown2018, @@ -69,8 +68,8 @@ @Book{rmarkdown2018 publisher = {Chapman and Hall/CRC}, address = {Boca Raton, Florida}, year = {2018}, - note = {ISBN 9781138359338}, - url = {https://bookdown.org/yihui/rmarkdown}, + isbn = {9781138359338}, + url = {https://yihui.org/rmarkdown/}, } @Book{rmarkdown2020, @@ -79,7 +78,7 @@ @Book{rmarkdown2020 publisher = {Chapman and Hall/CRC}, address = {Boca Raton, Florida}, year = {2020}, - note = {ISBN 9780367563837}, - url = {https://bookdown.org/yihui/rmarkdown-cookbook}, + isbn = {9780367563837}, + url = {https://yihui.org/rmarkdown-cookbook}, } From 8ec7de63fff046f36732f6cbeddaf1146a5301a3 Mon Sep 17 00:00:00 2001 From: Sarah Teichman Date: Mon, 22 Jun 2026 11:19:07 -0700 Subject: [PATCH 08/11] Stop tracking docs and update gitignore --- .gitignore | 1 + Clinical_Data_Management.Rproj | 17 ----------------- 2 files changed, 1 insertion(+), 17 deletions(-) delete mode 100644 Clinical_Data_Management.Rproj diff --git a/.gitignore b/.gitignore index a09926b..2405545 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,4 @@ spell_check_results.tsv docker/git_token.txt .Rproj.user/ docs/* +*.Rproj diff --git a/Clinical_Data_Management.Rproj b/Clinical_Data_Management.Rproj deleted file mode 100644 index 628359e..0000000 --- a/Clinical_Data_Management.Rproj +++ /dev/null @@ -1,17 +0,0 @@ -Version: 1.0 - -RestoreWorkspace: Default -SaveWorkspace: Default -AlwaysSaveHistory: Default - -EnableCodeIndexing: Yes -UseSpacesForTab: Yes -NumSpacesForTab: 2 -Encoding: UTF-8 - -RnwWeave: knitr -LaTeX: pdfLaTeX - -AutoAppendNewline: Yes - -BuildType: Website From 8af1eb9c196773a9c08d04eec1a8234d4c4c4da5 Mon Sep 17 00:00:00 2001 From: Sarah Teichman Date: Thu, 25 Jun 2026 15:44:49 -0700 Subject: [PATCH 09/11] fix clinical trials heading --- 01c-how_to_collect_data.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01c-how_to_collect_data.Rmd b/01c-how_to_collect_data.Rmd index debd86f..37c8dd9 100644 --- a/01c-how_to_collect_data.Rmd +++ b/01c-how_to_collect_data.Rmd @@ -109,7 +109,7 @@ Designing specific and sensitive CRFs for clinical studies requires a thoughtful Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. -## Clinical Trials +### Clinical Trials Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. 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b/docs/00-intro.md new file mode 100644 index 0000000..0075009 --- /dev/null +++ b/docs/00-intro.md @@ -0,0 +1,34 @@ +--- +title: "Clinical Data Management" +--- + + + + +# Introduction + +Clinical Data Management, Written and assembled by: Kate Isaac & Carrie Wright, Contributors: Christopher Battiston, David Hanauer, Guilherme Del Fiol, Harry Hochheiser, Jennifer Kelleher, Abby Robbertz, & Meghan McGrady + + +## Motivation + + +This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines. + +This course is intended to provide an overview of the different kinds of data commonly used in clinical research, the questions that can typically be asked with such data, as well as guidance for how to manage this data. + + +For individuals who: Have clinical data and don’t know what to do with it. Want a basic overview of what the clinical data represent and what questions can be asked.Need to know the basics of how to manage clinical data properly + + +## Topics Covered + +Concepts discussed in Clinical Data course: What does your clinical data type represent? How do you handle the clinical data securely and ethically? What types of analysis questions can one ask when it comes to different kinds of clinical data? Find resources, tools and tutorials to help you process and work with your data + + +## Curriculum + +The course will cover key underlying principles and concepts in ethical data handling. + +Overall Course Learning Objectives. 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It may come in many forms and its sensitive nature requires careful management by researchers. + +## Learning Objectives + +Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data + + + +## Clinical data is unique + +Clinical data refers to information __collected from patients__ during healthcare delivery, clinical trials, and medical research. Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data with predictable and consistent formats, clinical data is often heterogeneous, integrating many forms of both structured and unstructured data: quantitative measurements, categorical data, genotyping, images, subjective narratives from patient notes, and objective observations or conclusions. The unstructured nature of free text from notes reflecting subjective patient experiences or qualitative insights from healthcare professionals especially adds a further layer of complexity. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data's unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare. Further, because clinical data contains sensitive, personal information about patients, there are additional security and ethics concerns in the handling and management of clinical data. + +## Major clinical data types + +Clinical data can come in many different forms, including + +* patient demographics +* medical history or records such as diagnoses, lab results, vital signs, medication records, or procedure history +* genetic reports +* health monitor data +* images +* scanned documents, and notes written by physicians, nurses, and other clinicians +* survey/ case report form (CRF responses) +* and more ... + +Clinical data refers to information collected from patients during healthcare delivery, clinical trials, and medical research and could include demographics, medical history, lab results, imaging, treatment outcomes, and more + +Some sources of clinical data are more prevalent and readily obtainable than others. For instance, notes, demographics, images, and histories or observations/records (lab results, vitals, medications, procedures) are often stored directly in electronic medical record systems making them more easily accessible and so are the focus of most Electronic Health Record (EHR) data research efforts. + +### Structured data + +Observational records such as test results and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Structured data types frequently used in EHR research consist of comprehensive longitudinal records of a patient's interactions with a healthcare system and may include demographics, diagnoses, lab values, procedures, vitals, and medication records. + +Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. + +The structured nature of this data allows for it to be stored in tables which may be indexed by a patient or visit ID and often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. + +Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or "billing codes", provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. + +Example of structured data, a table that includes patient id numbers, billing codes, dates, blood pressure measurements, weight measurements, height measurements, and prescribed medications coded using the National Drug Code (NDC) + +As we've described it, structured clinical data is expected to have similarities, although specific details may vary based on the type of EHR being used and any customizations to the EHR for the specific environment or institution in which data was collected (e.g., any specialized pre-processing by institutional research offices prior to providing data to researchers). So it is important for researchers to consider differences present in their data if they've obtained it from multiple contexts or institutions. + +### Unstructured data / clinical notes + +Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. + + +Unstructured Data - Data without specific format: includes images, pathology reports, radiology reports, clinical notes, discharge summaries and more. Includes an image of an x-ray and some clinical notes that states: Patient reports a sharp pain in the right lower abdomen for the past 24 hours.Temperature 98.6°F, BP 120/80, tenderness in the right lower quadrant. Suspected appendicitis. Refer to surgery for evaluation, initiate IV fluids, and pain management. + + +When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [@Dhir2008] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. + + +## How to acquire clinical data + +### Secondary Sources + + +## Description of data + +### File types + +### Metadata + + +## Summary diff --git a/docs/01-data_types_files/figure-html/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ_g3385bea4ad0_0_0.png b/docs/01-data_types_files/figure-html/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ_g3385bea4ad0_0_0.png new file mode 100644 index 0000000000000000000000000000000000000000..4e606c1c720e542f24ed2c679e80038e82d8a9f5 GIT binary patch literal 52457 zcmeFYbyU@B)HTXRP(m7ML_$&;q+!z_-O}CN(kb1oNH+*bOG^kMAe)w!F6r*?;dtJ2 z&O62(sf2AIp=zIxT3ry8VV5#92^{)w3L`K9NdF1aB%P>Nbul4 z^+Jj-;Nbk>q{W0)-{}3$c=Q2BoVbUwm68-Aj-oY)BKO&|k@Q?7DHTlde54U!&qV51 zspx?td?U>+`5#nE=)ciWggsL`5?DFuY3+O7kUq2WD)Vmdt-jmZ+*B)h`*Xx=fB5%*4nvpzhnQ1gxoY;MfC4@CP>1+C;B_e ziPsF-B{_cp=hKK5|)}|8p?b0Lnk#|B!;f!TEQFO#JgFveOIH4a~pKiQRopku6&M 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Robbertz, Ph.D.^1^; and Meghan E. McGrady, Ph.D.^1,2^ + +**NOTE:** Jennifer Kelleher, Ph.D.^1^ and Abigail S. Robbertz, Ph.D.^1^ contributed equally. + +^1^ Center for Adherence and Self-Management, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA + +^2^ Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA + +The work discussed in this section was also supported by the National Cancer Institute at the National Institutes of Health (R21CA263704, K07CA200668) to MEM. JK and ASR are supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development at the National Institutes of Health (T32HD068223). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. + +::: + +Electronic monitoring devices are digital tools that can be used to track health behaviors over time such as: + +* Sleep +* Physical activity +* Medication adherence +* Calorie intake + +Electronic monitoring devices can also be used to assess physical health indicators including: +* Blood glucose levels +* Blood pressure +* Heart rate and heart rate variability +* Oxygen saturation + +Electronic monitoring devices enable researchers to track day-to-day health behaviors in the patient’s **"real-world" setting**. This allows researchers to explore patterns or changes in a patient’s health behavior and provides a richer understanding of **daily behavior over time**. + + +### Benefits of Monitoring Data + +1) Electronic monitoring devices often include data transmission abilities that enable healthcare providers or researchers to access these data in near **real-time** potentially informing intervention and/or medical decision-making. + +1) Electronic monitoring devices also have the potential to produce **more accurate** estimates of health behaviors than alternative strategies (e.g., self-report) as they are not subject to recall bias and can detect efforts to inflate adherence due to social desirability. + +### Considerations + +:::warning +This section is not exhaustive. Research teams are strongly encouraged to consult with experts with experience and training in collecting and analyzing data from specific devices. + +To ensure the outcome variables are aligned with the research question of interest and [ethical and age/developmental considerations](https://pmc.ncbi.nlm.nih.gov/articles/PMC10798216/) (@psihogios_ethical_2024; @modi_pediatric_2012) have been appropriately accounted for, readers are encouraged to consult with researchers in their field who have integrated these measurement strategies into their work. +::: + +#### Medication Adherence + +There are three major components of medical adherence (the tracking of taking medication): + +- Initiation: Starting a prescribed regimen +- Implementation: The amount of which a patient's medication-taking behavior corresponds with the treatment regimen or protocol +- Discontinuation: Stopping a perscribed regimen + +For more information see: + +- [A new taxonomy for describing and defining adherence to medications (Vrijens et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC3403197/) +- [Pediatric self-management: A framework for research, practice, and policy. 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zPlXR?P7T;YxyNe&I5m?+(}fp8Pkc4JgGq!n^3dwwv89ty8l*tM+9aS`k%{?jqVp;J z+gLkOcXjQwZONm+qRc|3J^*thk-f>eE9{=B(gRG+vvSGAjD(3Meu5X~>Dx7n6!RZl`xKC4Qej0SdK{9NXTcfm^N}A^q&$a{} zDQtnIHsXk*alKRtO97j50_>?q)zvm>6Oeo>^L!<8u7?F@)B~>+O3eP5xby(63b2dA zQFTiBso*=hI;F77Ja-Sm0FWm1!C9YZ;KfpD<<(wviQagKv&(^H2d{z;5lU?Aov*d5 zlZhu*{Lr}3exSvO4E#Rgd|_AFP=)v4liG>%TAEcbpc_(mq<$+uvtYhe;IbQ+E~}zq zR~@5?Rf1Zpd#yMTQ%^1U{M@3;gY-f3+>Z)>Bv2>&vvMhHD5IbDHS28cYFq+x>~!Zl z)HAB4Kfvc3-(3*j-U386Ax;}A?i$Ds5hK-gy_u~tc`*V`B6?b75*>StSJ?$<(nYJO z`Lpf4L~gXq8sNoXy`bnpcX-;g?^3GX9MqwfTAAUOZpNWT+myEgzDS0=|I01}&FjQgm8r_PGXM1K9k(ym{O-5p4`qJ*rgxup* z7}oqxBsbK@$3fb19Gr`Y4j}fVOKLAdhdZGB?|<%g*h0H$%P~9A{DU}jmP1-AleRRd z9z59K1XO@YD6j)gKB&GVsg*NM{elfH^i{%Ebje!zRrmqgNV;WiruLj5Yl0a3x)ZP# z*KwZcf`bca9O4r@kO2O(`lqkV_se+>BQa=9g}5;zrhGeTxT!x1&LCN*#rXi;>RZ`w z-DC##B0IwLN7Mj_<#LX$o<>lFo+_9KZys8gUx*sK*A#< sJC5ceBcs4lkX;Av8JX+ + +You can also save these plots to file: + + +```r +png(file.path(output_dir, "test_plot.png")) +hist_plot +``` + +``` +## $breaks +## [1] 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 +## +## $counts +## [1] 5 27 27 30 31 18 6 6 +## +## $density +## [1] 0.06666667 0.36000000 0.36000000 0.40000000 0.41333333 0.24000000 0.08000000 +## [8] 0.08000000 +## +## $mids +## [1] 4.25 4.75 5.25 5.75 6.25 6.75 7.25 7.75 +## +## $xname +## [1] "iris$Sepal.Length" +## +## $equidist +## [1] TRUE +## +## attr(,"class") +## [1] "histogram" +``` + +```r +dev.off() +``` + +``` +## png +## 2 +``` + +### Image example + + +How to include a Google slide. It's simplest to use the `leanbuild` package: + +Major point!! example image + +But if you have the slide or some other image locally downloaded you can also use html like this: + +Major point!! example image + + +### Video examples + +You can use `knitr::include_url()` like this: + + +```r +knitr::include_url("https://www.youtube.com/embed/VOCYL-FNbr0") +``` + + + +OR this works: + + + +### Links to files + +This works: + + + +Or this: + +[This works](https://www.messiah.edu/download/downloads/id/921/Microaggressions_in_the_Classroom.pdf). + +Or this: + + + +### Links to websites + +Examples of including a website link. + +This works: + + +```r +knitr::include_url("https://yihui.org") +``` + + + +OR this: + +![Another link](https://yihui.org) + +OR this: + + + +### Citation examples + +We can put citations at the end of a sentence like this [@rmarkdown2021]. +Or multiple citations [@rmarkdown2021, @Xie2018]. + +but they need a ; separator [@rmarkdown2021; @Xie2018]. + +In text, we can put citations like this @rmarkdown2021. + +## Print out session info + + +```r +sessionInfo() +``` + +``` +## R version 4.0.2 (2020-06-22) +## Platform: x86_64-pc-linux-gnu (64-bit) +## Running under: Ubuntu 20.04.2 LTS +## +## Matrix products: default +## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so +## +## locale: +## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C +## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 +## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C +## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C +## [9] LC_ADDRESS=C LC_TELEPHONE=C +## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C +## +## attached base packages: +## [1] stats graphics grDevices utils datasets methods base +## +## other attached packages: +## [1] magrittr_1.5 +## +## loaded via a namespace (and not attached): +## [1] knitr_1.33 hms_0.5.3 R6_2.4.1 rlang_0.4.10 +## [5] httr_1.4.2 stringr_1.4.0 highr_0.8 tools_4.0.2 +## [9] xfun_0.26 jquerylib_0.1.1 htmltools_0.5.0 ellipsis_0.3.1 +## [13] yaml_2.2.1 leanbuild_0.1.2 digest_0.6.25 tibble_3.0.3 +## [17] lifecycle_1.0.0 crayon_1.3.4 bookdown_0.24 readr_1.4.0 +## [21] vctrs_0.3.4 fs_1.5.0 curl_4.3 evaluate_0.14 +## [25] rmarkdown_2.10 stringi_1.5.3 compiler_4.0.2 pillar_1.4.6 +## [29] pkgconfig_2.0.3 +``` diff --git a/docs/02-data_types.md b/docs/02-data_types.md new file mode 100644 index 0000000..5198f60 --- /dev/null +++ b/docs/02-data_types.md @@ -0,0 +1,40 @@ +--- +title: Clinical Data Types +--- + + + + +# Clinical Data Types + +## Learning Objectives + + +## Patient Records: Electronic Health Records + +Clinical data can come in many different forms, including demographics, diagnoses, lab results, vital signs, medication records, procedures, genetic reports, images, scanned documents, and notes written by physicians, nurses, and other clinicians. Although any of these types of information might theoretically be available for use in clinical research, some sources are more accessible than others. As they are often stored directly in electronic medical record systems, notes, demographics, observations (labs, medications, procedures, and vitals), and images are the easiest data to work with, and are the focus of most Electronic Health Record (EHR) data research efforts. + +### Structured Data + +Observational and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Although details may vary based on the type of EHR being used, the customizations to the EHR for the specific environment in which data was collected, and any pre-processing that might be done by institutional research offices prior to providing data to researchers, details are likely to be familiar across contexts. Structured data types frequently used in EHR research include demographics, diagnoses, lab values, procedures, vitals, and medication records. Often provided as tables indexed off of a patient or visit ID, these tables +often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. + +Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or "billing codes", provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. + +### Unstructured Data / Clinical Notes + +Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. + +When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [INSERT REF HERE] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. + +## Physiological + +## Radiology + +## Pathology + +## Secondary Sources + +## Discussion + +## Conclusion diff --git a/docs/02-data_uses.md b/docs/02-data_uses.md new file mode 100644 index 0000000..9c47a0f --- /dev/null +++ b/docs/02-data_uses.md @@ -0,0 +1,395 @@ +--- +title: Clinical Data Uses +--- + + + +# Clinical Data Uses + +## Learning Objectives + +## General uses of clinical data + +### Cancer Prevention and Care + +The near universal adoption of electronic health record (EHR) systems in the US has created unprecedented opportunities to improve cancer prevention and care. As described in previous chapters, EHR systems store comprehensive longitudinal records of a patient's interactions with a healthcare system, including data about: + + - demographics + - conditions + - family history + - clinical notes + - laboratory test results + - medications + - procedures + - imaging + - genetic test reports + + Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. + +These data can be accessed not only by health professionals, but also by patients through patient portals. EHR data can also be used to enable data-driven interventions such as provider- and patient-facing clinical decision support (CDS) and population health management (PHM). + +CDS has been defined as tools that "provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care" [@Osheroff_2007]. Examples of widely adopted CDS tools with demonstrated effectiveness for cancer prevention, diagnosis, and care include: + + * provider and patient reminders for cancer screening + * decision aids and shared decision-making tools (e.g., for prostate cancer treatment, lung cancer screening) + * AI-supported diagnostic imaging + * chemotherapy decision support + * at-home symptom care + +While CDS tools generally provide decision support focused on a specific patient at a time, PHM are strategies that target specific patient populations [@Swarthout_Bishop_2017]. PHM efforts generally consist of: + + * population algorithms that are applied over EHR and other data sources to identify individuals who are eligible for a specific healthcare intervention (e.g., colorectal cancer screening, tobacco cessation, HPV vaccination) + * patient engagement strategies (e.g., patient portals, text messaging, chatbots, patient navigators) that provide education and access to those interventions + * analytic tools that assess the effectiveness of the PHM program. + +Several PHM programs have demonstrated to be effective in increasing the uptake of cancer prevention. For example, the colorectal cancer screening program at Kaiser Permanente uses digital (i.e., text messaging, patient portal), mailed, and patient navigation approaches to increase colorectal cancer screening by mailing Fecal Immunohistochemical Test (FIT) kits to patients' homes [@Gupta2020]. Also the Cancer Moonshot BRIDGE trial used the GARDE platform [@Bradshaw2022] (ITCR-funded) to identify candidates for genetic testing of hereditary cancer syndromes based on EHR data; and for patient outreach, pre- and post-test education via automated chatbots [@Kaphingst2024]. + + +Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. + +Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. + +While some CDS and PHM approaches have been successfully adopted widely, emerging technologies such as the use of generative AI approaches to analyze diagnostic imaging, large language models (LLMs) to extract information from narrative texts (e.g., clinical notes), LLM-based chatbots to communicate with patients, and digital health tools such as home-based sensors are creating unprecedented opportunities for next generation CDS and PHM. These approaches have the potential to enable significant breakthroughs through the implementation of patient-tailored cancer prevention and care at a population scale. Nevertheless, substantial research is needed to ensure effective and fair implementation of these CDS and PHM interventions. + + + +## Types of questions that can be asked with clinical data + +### Risk Prediction + +Risk prediction in clinical research involves using data to assess the likelihood of certain outcomes or events occurring in patients. This could include predicting the risk of developing a particular disease, experiencing a specific complication, or responding to a treatment. + +Data used for risk prediction can come from various sources, including: + + * Clinical Data: This includes patient demographics, medical history, laboratory results, and imaging studies. + * Genetic Data: Genetic information, such as DNA sequencing results, can provide valuable insights into an individual's susceptibility to certain diseases. + * Environmental and Lifestyle Data: Factors such as diet, exercise habits, smoking status, and environmental exposures can influence disease risk and may be included in risk prediction models. + * Biomarkers: Biological markers indicative of disease or physiological processes can be used as predictors in risk models [@Bodaghi_Fattahi_Ramazani_2023]. + +Once relevant data is collected, statistical and machine learning techniques can be applied to develop predictive models. These models aim to identify patterns and relationships within the data that are associated with the outcome of interest. Common techniques include logistic regression, decision trees, random forests, support vector machines, and neural networks. + +After the model is trained on a dataset, it can be validated using independent datasets to assess its performance and generalizability. Once validated, the model can be used to predict risk in new patients based on their individual characteristics and data. + +Clinical prediction rules are a subset of risk prediction models, specific to clinical research. + +Examples of risk prediction models ... + +Risk prediction models are important because ... + +Overall, risk prediction in clinical research allows healthcare professionals to identify individuals at higher risk of certain outcomes, enabling targeted interventions, personalized treatments, and more efficient resource allocation. + +### Cohort identification for research + +Clinical data plays a crucial role in cohort identification for research purposes. Researchers typically use electronic health records (EHRs), medical databases, or registries to identify cohorts based on specific criteria such as age, gender, medical conditions, treatments, medications, and outcomes. Advanced data mining and natural language processing techniques can also be employed to extract relevant information from unstructured data sources like clinical notes. Once cohorts are identified, researchers can analyze the data to study disease progression, treatment effectiveness, and outcomes. + +Cohort identification is important regardless of research study type, but to provide specific examples: + +* Research Design: Identifying cohorts allows researchers to design studies with appropriate inclusion or exclusion criteria. By selecting specific groups of individuals with similar characteristics or exposures, researchers can investigate hypotheses effectively. +* Clinical Insights: Cohort studies enable researchers to observe the natural history of diseases, track outcomes over time, and assess the effectiveness of interventions or treatments. Understanding how different factors influence disease progression or treatment response can inform clinical decision-making and improve patient care. +* Epidemiological Studies: Cohort identification is crucial for epidemiological research to understand the incidence, prevalence, and risk factors associated with diseases. By following cohorts over time, researchers can identify trends, patterns, and associations that contribute to our understanding of disease causation and prevention. +* Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. +* Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. + +### Case report forms (CRFs) + +In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter -- specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). + +Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on **accessibility, sensitivity, specificity, and comfort for people** + +::: {.notice} + +While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren't necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions -- the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. + +::: + +Here, we explore the different categories of questions that may be included within case report forms: + +1. **Demographic and Socioeconomic Questions** + +Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. + +- **Sex and Gender**: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. +- **Ethnicity and Race**: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. +- **Socioeconomic Status**: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, "What is your current employment status?" with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. + +It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. + +2. **Health and Medial History Questions** + +Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. + +- **Medical Conditions and History**: Questions about past and present health conditions should use clear, accessible language. For example, "Have you ever been diagnosed with any of the following conditions? (Please check all that apply)" followed by a comprehensive list containing all necessary options. +- **Medication Use**: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. +- **Disability and Functional Status**: For many populations, it is important to use person-first language, such as "Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?" and provide space for participants to describe their specific needs. However, different populations have different preferences. The "Disability Language Style Guide" from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic [@ncdj_style_guide]. + +Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. + +3. **Experience and Quality of Life Questions** + +Understanding how health conditions and treatments affect participants' daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. + +- **Daily Living and Social Functioning**: Questions like "How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?" can help assess the impact on daily life, with options ranging from "never" to "always." +- **Emotional and Psychological Well-Being**: Including questions such as "In the past week, how often have you felt anxious or depressed?" using a scale from "not at all" to "very often" can provide insights into mental health needs. +- **Support Systems and Social Networks**: Asking about social support (e.g., "Do you have someone you can rely on for emotional support?") can help identify participants' needs for social and emotional resources. + +Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. + +4. **Sexual and Reproductive Health Questions** + +Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. + +- **Sexual Orientation and Gender Identity (SOGI)**: Instead of just predefined categories, open-ended questions like "How would you describe your sexual orientation?" and "What is your gender identity?" allow participants to self-identify. Offering the option to skip these questions respects participants' privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. +- **Reproductive Health**: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, "Are you currently using any form of contraception? If yes, please specify." +- **Sexual Activity and History**: Questions should be direct but framed sensitively, such as "Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs." + +These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. + +5. **Treatment Preferences and Decision-Making Questions** + +Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. + +- **Decision-Making Preferences**: Questions like "How involved would you like to be in decisions about your healthcare?" offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. +- **Cultural and Religious Considerations**: Asking, "Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?" ensures that care is respectful and culturally appropriate. +- **Treatment Burden**: Questions such as "What level of inconvenience or side effects would be acceptable to you when considering a treatment?" help to gauge participants' preferences and comfort levels. + +These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. + +6. **Accessibility and Accommodation Needs Questions** + +To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. + +- **Language and Communication Needs**: "What is your preferred language for communication? Do you need an interpreter or translated materials?" These questions help ensure that participants can understand the materials. +- **Physical Accessibility**: Asking, "Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?" ensures physical accessibility. +- **Format Preferences**: "Would you prefer to complete this form online, on paper, or verbally with assistance?" helps accommodate different needs and preferences. +Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. + +Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. + +7. **Cultural Sensitivity and Identity Questions** + +CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. + +- **Cultural Identity and Practices**: An open-ended question such as "Are there any cultural practices or beliefs that are important for us to be aware of in your care?" allows participants to share relevant information. +- **Dietary Restrictions and Preferences**: Asking, "Do you have any dietary restrictions or preferences that are culturally or religiously motivated?" ensures that these are respected. +- **Community and Belonging**: "Is there anything about your community or background that you would like us to know to provide better care?" encourages participants to share relevant aspects of their identity. + +These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. + +8. **Ensuring Comfort, Trust, and Privacy Questions** + +Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. + +- **Comfort and Confidentiality**: Asking, "Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?" helps build trust. +- **Feedback and Preferences**: "Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?" invites participants to share their feedback. +- **Consent and Voluntary Participation**: Questions like, "Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary." reinforce autonomy and respect. + +Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. + +::: {.warning} + +The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. + +::: + +Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. By thoughtfully addressing both unique study requirements and universally relevant data points, researchers can optimize CRFs for consistency across clinical studies. + + +### Clinical studies and trials + +Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. + +Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. + +::: {.definition} +The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." +::: + +The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. + +::: {.definition} +**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. +::: + +Here, we explore the different categories of questions that can be addressed using clinical trials data, ranging from the evaluation of treatment efficacy and safety to the exploration of predictive factors for disease prognosis and the personalization of medical care. + +1. **Efficacy and Effectiveness Questions** + +One of the primary objectives of clinical trials is to determine the **efficacy of new treatments or interventions**. Clinical data is used to answer fundamental questions such as "Is the new treatment effective?" and "How does its effectiveness compare to existing treatments?" + +Researchers design studies to measure the **magnitude of treatment effects**, comparing outcomes between treatment and control groups (e.g., a new drug versus a placebo or standard care). + +Beyond controlled settings, data from real-world evidence (RWE) studies can further address questions about the effectiveness of treatments when applied in routine clinical practice. For example, "Is Drug A more effective than Drug B in managing hypertension in a real-world patient population?" Such questions help to assess how treatments perform outside the controlled environment of clinical trials where patient adherence comorbidities, and polypharmacy may influence outcomes. While clinical trials are an important source of real-world data (RWD), they are typically conducted under tightly controlled conditions, which may not fully reflect the complexities of real-world studies. This makes RWE particularly valuable for understanding how treatments work when patients have varying levels of adherence or multiple chronic conditions that are often excluded from clinical trails. + +::: {.definition} +**Comorbidities**: refer to the presence of one or more additional medical conditions or diseases that coexist with a primary condition in a patient. These conditions can occur independently or be related to the primary disease. +::: + +2. **Safety and Tolerability Questions** + +Safety is a critical aspect of any clinical study, and clinical data is essential in identifying and characterizing the safety profile of new treatments. Questions like "What are the common adverse effects of the new treatment?" or "What is the incidence of serious adverse events (SAEs) in patients receiving Drug A?" are addressed by carefully monitoring and recording adverse events throughout the study. Comparative safety questions also arise, such as "Does Drug A have fewer adverse effects compared to Drug B?" These questions are crucial in weighing the benefits of a treatment against its risk and in identifying specific populations that may be at higher risk of adverse reactions. + +::: {.definition} +**Serious Adverse Events (SAEs)**: Any medical occurrence during a clinical trial or medical treatment that results in significant negative outcomes, such as death, life-threatening situations, hospitalization (or its prolongation), persistent or significant disability, or congenital anomalies. SAEs also include any event that requires intervention to prevent one of these outcomes. +::: + +::: {.definition} +**Incidence**: Refers to the rate or number of new cases of SAEs that occur within a specific population during a defined time period, typically during a clinical trial or study. Is it usually expressed as a proportion, such as the number of SAEs per 100 or 1,000 participants, and helps measure how frequently these serious events happen among those exposed to a particular treatment or intervention. +::: + +3. **Comparative Effectiveness and Cost-Effectiveness Questions** + +Beyond safety and efficacy, clinical data can be used to evaluate the comparative effectiveness of different treatments and their cost-effectiveness. These questions often guide healthcare policy and clinical guidelines: "Is Drug A more cost-effective than standard care for managing chronic heart failure?" and "What is the cost per quality-adjusted life year (QALY) gained with the new intervention?" Such analyses provide valuable information for stakeholders, including healthcare providers, payers, and policymakers, to make informed decisions on resource allocation. + +::: {.definition} +**Cost per quality-adjusted life year (QALY)** is a metric used in health economics to assess the value of a medical intervention by measuring the cost of gaining one year of life adjusted for its quality. A QALY incorporates both the **quantity** and **quality** of life lived, where one QALY is equivalent to one year of life in perfect health. If a treatment improves both the length and quality of life, it earns more QALYs. The **cost per QALY** is calculated by dividing the cost of the treatment by the number of QALYs gained, helping to determine the cost-effectiveness of healthcare interventions. Lower cost per QALY indicates better value for money in terms of health benefits achieved [-@Drummond_Sculpher_Claxton_Stoddart_Torrance_2015]. +::: + +4. **Mechanistic and Biomarker Questions** + +Clinical studies often explore not just whether a treatment works, but also how it works. Mechanistic questions delve into the underlying biological pathways affected by a treatment. For instance, "Does Drug A work by inhibiting a specific enzyme involved in the disease process?" Biomarker data can play a key role in such analyses, helping to identify molecular markers that predict response to treatment. Questions like "Are there biomarkers that can predict which patients are more likely to benefit from Drug A?" help in developing targeted therapies and personalized treatment approaches. + +5. **Predictive and Prognostic Questions** + +Clinical trials and studies frequently aim to identify factors that predict disease outcomes or treatment responses. For example, "What baseline characteristics predict a better response to Drug A?" or "What are the predictors of mortality in patients with severe heart failure?" Such prognostic and predictive questions are crucial for identifying high-risk patients, guiding treatment decisions, and developing clinical guidelines. Identifying patient subgroups that benefit more or less from a treatment also facilitates personalized medicine approaches, ensuring that the right treatment is delivered to the right patient at the right time. + +With the advent of personalized medicine, clinical data is increasingly being used to answer questions tailored to individual patient profiles. For instance, "What patient characteristics modify the effect of the treatment?" or "Can we predict which patients are most likely to benefit from Drug A using machine learning models?" These questions are at the forefront of precision medicine, where the goal is to customize healthcare to each patient based on their unique genetic, biomarker, and clinical characteristics. + +6. **Quality of Life and Patient-Reported Outcome Questions** + +In modern clinical research, the impact of treatments on patients' quality of life and their subjective experiences has gained prominence. Questions such as "How does Drug A affect patients' quality of life compared to standard care?" or "What are the patient-reported outcomes associated with the new intervention?" are increasingly addressed in clinical trials. Data from quality of life assessments which are standardized and validated questionnaires (for example, the Health Assessment Questionnaire or HAQ), patient-reported outcome measures (PROMs, tools or questionnaires used to assess a patient's health status or health-related quality of life directly from the patient's perspective), and other patient-centric endpoints (Smoking History, Surgical History, etc.) provide valuable insights into how treatments affect patients beyond traditional clinical endpoints. + +7. **Longitudinal and Follow-Up Questions** + +Long-term follow-up studies are essential for understanding the durability of treatment effects and any delayed adverse effects. Questions like "What are the long-term outcomes associated with Drug A?" and "Is there a sustained benefit of Drug A in reducing symptoms after five years?" are vital for assessing the overall value of treatments. Such longitudinal analyses help in determining the optimal duration of therapy, the need for maintenance treatments, and the overall benefit-risk profile of an intervention. + +8. **Safety in Special Populations Questions** + +Clinical data can also be used to evaluate the safety and efficacy of treatments in special populations, such as children, pregnant women, or elderly patients. Questions such as "Is Drug A safe for use in pregnant women?" and "What is the risk of adverse events when Drug A is administered to patients with multiple comorbidities?" are essential for developing age- and condition-specific clinical guidelines. These questions help ensure that treatments are tailored to the needs of different patient populations, minimizing harm and maximizing benefits. + +9. **Adherence and Compliance Questions** + +Patient adherence to prescribed treatment regimens can significantly impact the outcomes of clinical studies. Questions like "What factors influence patient adherence to the new treatment?" and "How does adherence affect treatment efficacy?" are addressed through analyzing adherence data through monitors if a regulated study, or with interim/baseline analysis if non-regulated. Understanding these factors can lead to interventions that improve adherence and, consequently, the effectiveness of the treatment. + +10. **Real-World Evidence and Generalizability Questions** + +Real-world evidence (RWE) studies help bridge the gap between clinical trial results and clinical practice. Questions such as "How generalizable are the results of this clinical trial to the broader population?" or "What is the impact of real-world use patterns on treatment outcomes?" are essential for understanding how well clinical trial findings apply to everyday clinical settings. These questions help determine if the benefits observed in clinical trials can be replicated in diverse patient populations under routine care conditions. + +11. **Prevention and Risk Reduction Questions** + +Clinical data is also crucial in answering questions related to prevention and risk reduction strategies. For example, "Can the new treatment reduce the risk of developing diabetes in high-risk individuals?" or "What factors are associated with a reduced risk of cardiovascular disease in a large cohort study?" These questions are fundamental in preventive medicine, guiding public health interventions and informing clinical practice. + + +Clinical Data provides a wealth of information that can be leveraged to answer a broad array of questions in clinical trials and studies. From understanding treatment efficacy and safety to exploring long-term outcomes, quality of life impacts, and the potential for personalized medicine, clinical data is foundational to advancing medical knowledge and improving patient care. Case report forms (CRFs) are instrumental in identifying patient cohorts or subgroups, documenting baseline characteristics, and capturing information on comorbidities, while also monitoring adherence data, which are essential for ensuring accurate and meaningful analysis. The type of questions that can be asked and answered are continually evolving as new data sources, analytical methods, and research paradigms emerge, further enriching the field of clinical research. + + +### Retrospective analysis + +Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. + +Here, we explore the different categories of questions that can be addressed using retrospective data analysis: + +1. **Descriptive Questions** + +Descriptive questions aim to summarize and describe the characteristics of a patient population, disease, or healthcare process. They provide a foundational understanding of a dataset and are often the starting point for more complex analyses. + +- **Patient Demographics and Characteristics**: What are the demographic profiles (age, gender, ethnicity, socioeconomic status) of patients diagnosed with a specific condition? What are the common comorbidities and risk factors in this population? +- **Disease Prevalence and Incidence**: What is the prevalence or incidence of a specific disease or condition in a particular population or geographic area over a defined period? +- **Clinical Presentation**: What are the most common presenting symptoms, clinical signs, or laboratory findings associated with a specific disease or condition? + +These questions help in understanding the baseline characteristics and epidemiology of diseases, which is crucial for planning further studies or developing public health interventions. + +2. **Treatment and Intervention Questions** + +Retrospective data can be used to examine the real-world effectiveness, safety, and patterns of treatment and interventions. These questions are critical for understanding how treatments are applied in clinical practice and their outcomes: + +- **Treatment Effectiveness**: What is the effectiveness of a specific treatment or intervention in reducing symptoms, improving quality of life, or achieving clinical outcomes in a real-world setting? +- **Adherence and Persistence**: What are the rates of adherence and persistence with a prescribed treatment regimen over time? What factors are associated with higher or lower adherence rates? +- **Comparative Effectiveness**: How does the effectiveness of one treatment compare to another in a similar population? What are the relative benefits and risks associated with different treatment options? + +By examining historical data, researchers can gain insights into how different treatments perform outside the controlled environment of clinical trials, thereby informing clinical decision-making and guidelines. + +3. **Outcome and Prognostic Questions** + +Understanding patient outcomes and prognostic factors is central to retrospective analyses. These questions focus on the end results of healthcare practices and patient management, including survival, complications, and quality of life: + +- **Survival and Mortality**: What are the survival rates and mortality rates associated with a particular disease, condition, or treatment? What factors are associated with increased or decreased survival rates? +- **Complication Rates**: What are the rates and types of complications associated with specific diseases, surgeries, or treatments? Are there identifiable risk factors for these complications? +- **Prognostic Factors**: What are the key prognostic factors (e.g., age, stage of disease, comorbidities) that influence the outcomes of patients with a particular condition? + +These questions help identify factors that influence patient outcomes, guiding clinicians in predicting disease progression and tailoring individual treatment plans. + +4. Risk Factor and Predictive Modeling Questions + +Retrospective analyses are often used to identify potential risk factors for diseases and develop predictive models. These questions aim to uncover associations between variables and outcomes to help predict future events: + +- **Risk Factors for Disease**: What are the risk factors associated with developing a particular disease or condition? Are there demographic, genetic, behavioral, or environmental factors that significantly increase the risk? +- **Prediction Models**: Can we develop a predictive model to estimate the likelihood of disease progression, relapse, or adverse outcomes based on historical patient data? +- **Multivariable Analysis**: How do multiple factors interact to influence the risk of an outcome? Are there synergistic or antagonistic effects between different risk factors? + +These questions are critical for developing tools that help clinicians assess risk and make more informed decisions in preventive care and early intervention. + +5. **Healthcare Utilization and Cost Questions** + +Understanding how healthcare resources are utilized and the associated costs is important for optimizing healthcare delivery. Retrospective data can provide valuable insights into patterns of care and resource allocation for questions such as: + +- **Healthcare Resource Utilization**: What are the patterns of healthcare utilization (e.g., hospitalizations, emergency room visits, outpatient visits) for patients with a specific condition? How does utilization vary by patient demographics, disease severity, or geographic location? +- **Cost of Care**: What are the direct and indirect costs associated with managing a specific disease or condition? How do different treatment options impact overall healthcare costs? +- **Cost-Effectiveness**: How does the cost-effectiveness of one treatment or intervention compare to another? What are the economic impacts of different clinical management strategies? + +These questions can inform healthcare policy, budget planning, and the development of strategies to improve cost-effectiveness and access to care. + +6. **Quality of Care and Patient Safety Questions** + +Retrospective analysis can be used to evaluate the quality and safety of care provided to patients, identifying areas for improvement and potential patient safety concerns, helping to answer questions such as: + +- **Quality Indicators**: Quality indicators are standardized measures of health care quality and may focus on more specialized groups of care like preventative, inpatient or emergency care, pediatric or maternal health, etc. What are the levels of observed mortality or morbidity for a certain population in a specific setting? How utilized are certain procedures? [@AHRQ] +- **Adverse Events and Safety**: What is the incidence of adverse events, medication errors, or healthcare-associated infections in a particular patient population? What factors are associated with higher or lower rates of adverse events? +- **Patient Satisfaction and Experience**: What are the reported levels of patient satisfaction with healthcare services? How does satisfaction vary by demographic or clinical characteristics? + +These questions help in assessing healthcare quality, identifying gaps in care, and developing interventions to enhance patient safety and satisfaction. + +7. **Temporal and Trend Analysis Questions** + +Retrospective studies often involve analyzing temporal patterns to understand how diseases, treatments, or outcomes change over time. These questions help in recognizing trends and patterns that can inform future research and policy decisions: + +- **Temporal Trends in Disease Incidence and Prevalence**: How have the incidence and prevalence of a specific disease changed over the past decade? Are there seasonal or cyclical patterns in disease occurrence? +- **Trends in Treatment Practices**: How have treatment practices and prescribing patterns evolved over time? Are there emerging trends in the use of certain medications or interventions? +- **Trends in Outcomes and Survival**: How have patient outcomes, such as survival rates or complication rates, changed over time with advances in treatment and healthcare delivery? + +These questions are essential for monitoring public health trends, evaluating the impact of interventions, and planning future healthcare strategies. + +8. **Subgroup and Stratified Analysis Questions** + +Subgroup analyses can help uncover differences in disease characteristics, treatment responses, and outcomes among different patient groups. This type of analysis is particularly valuable for personalized medicine and addressing questions related to health disparities: + +- **Differences by Demographic Group**: How do treatment outcomes, risk factors, or disease characteristics differ between various demographic groups (e.g., age, gender, race/ethnicity)? +- **Subgroup-Specific Effectiveness**: Does a particular treatment work better in one subgroup of patients compared to another? What factors might explain these differences? +- **Health Disparities**: Are there disparities in healthcare access, treatment outcomes, or disease burden across different socioeconomic or minority groups? + +Questions focusing on subgroups are crucial for understanding variations in care and outcomes, identifying at-risk populations, and developing targeted interventions. + + +Retrospective analysis of clinical data allows researchers to ask a wide range of questions that provide valuable insights into patient characteristics, disease epidemiology, treatment effectiveness, outcomes, risk factors, healthcare utilization, and more. By carefully formulating questions and analyzing historical data, researchers can uncover patterns, identify trends, and generate evidence that informs clinical practice, healthcare policy, and future research directions. This chapter outlines the various categories of questions that can be addressed through retrospective analysis, highlighting the potential of this approach to advance knowledge and improve patient care in diverse clinical settings. + + +## Research Design Considerations + +Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. + +One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, + +* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. +* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. +* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. +* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. + +Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: + +* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. +* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. +* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. +* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. + +With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. + 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You may want to try searching to find the page's new location, or use +the table of contents to find the page you are looking for.

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+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
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+ + + + + + + + + + + + + diff --git a/docs/About.md b/docs/About.md new file mode 100644 index 0000000..075005a --- /dev/null +++ b/docs/About.md @@ -0,0 +1,119 @@ +--- +title: "Clinical Data Analysis for Cancer Informatics" +--- + +# About the Authors {-} + +These credits are based on our [course contributors table guidelines](https://github.com/jhudsl/DaSL_Course_Template_Bookdown/wiki/How-to-give-credits). + +  +  + +|Credits|Names| +|-------|-----| +|**Pedagogy**|| +|Lead Content Instructor(s)|[FirstName LastName](link to personal website)| +|Lecturer(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involved| Delivered the course in some way - video or audio| +|Content Author(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involved | If any other authors besides lead instructor| +|Content Contributor(s) (include section name/link in parentheses) - make new line if more than one section involved| Jennifer Kelleher, Ph.D.1*; Abigail S. Robbertz, Ph.D.| +|Content Editor(s)/Reviewer(s) | Julia K. Herriott, B.S.| +|Content Director(s) | Helped guide the content direction| +|Content Consultants (include chapter name/link in parentheses or word "General") - make new line if more than one chapter involved | Gave high level advice on content| +|Acknowledgments| Gave small assistance to content but not to the level of consulting | +|**Production**|| +|Content Publisher(s)| Helped with publishing platform| +|Content Publishing Reviewer(s)| Reviewed overall content and aesthetics on publishing platform| +|**Technical**|| +|Course Publishing Engineer(s)| Helped with the code for the technical aspects related to the specific course generation| +|Template Publishing Engineers|[Candace Savonen](https://www.cansavvy.com/), [Carrie Wright](https://carriewright11.github.io/)| +|Publishing Maintenance Engineer|[Candace Savonen](https://www.cansavvy.com/)| +|Technical Publishing Stylists|[Carrie Wright](https://carriewright11.github.io/), [Candace Savonen](https://www.cansavvy.com/)| +|Package Developers ([Leanbuild](https://github.com/jhudsl/leanbuild))|[John Muschelli](https://johnmuschelli.com/), [Candace Savonen](https://www.cansavvy.com/), [Carrie Wright](https://carriewright11.github.io/)| +|**Art and Design**|| +|Illustrator(s)| Created graphics for the course| +|Figure Artist(s)| Created figures/plots for course| +|Videographer(s)| Filmed videos| +|Videography Editor(s)| Edited film| +|Audiographer(s)| Recorded audio| +|Audiography Editor(s)| Edited audio recordings| +|**Funding**|| +|Funder(s)| Institution/individual who funded course including grant number| +|Funding Staff| Staff members who help with funding| + +  + + +``` +## ─ Session info ─────────────────────────────────────────────────────────────── +## setting value +## version R version 4.3.2 (2023-10-31) +## os Ubuntu 22.04.4 LTS +## system x86_64, linux-gnu +## ui X11 +## language (EN) +## collate en_US.UTF-8 +## ctype en_US.UTF-8 +## tz Etc/UTC +## date 2026-05-10 +## pandoc 3.1.1 @ /usr/local/bin/ (via rmarkdown) +## +## ─ Packages ─────────────────────────────────────────────────────────────────── +## package * version date (UTC) lib source +## bookdown 0.46 2025-12-05 [1] CRAN (R 4.3.2) +## bslib 0.6.1 2023-11-28 [1] RSPM (R 4.3.0) +## cachem 1.0.8 2023-05-01 [1] RSPM (R 4.3.0) +## cli 3.6.5 2025-04-23 [1] CRAN (R 4.3.2) +## devtools 2.4.5 2022-10-11 [1] RSPM (R 4.3.0) +## digest 0.6.34 2024-01-11 [1] RSPM (R 4.3.0) +## ellipsis 0.3.2 2021-04-29 [1] RSPM (R 4.3.0) +## evaluate 1.0.5 2025-08-27 [1] CRAN (R 4.3.2) +## fastmap 1.1.1 2023-02-24 [1] RSPM (R 4.3.0) +## fs 1.6.3 2023-07-20 [1] RSPM (R 4.3.0) +## glue 1.7.0 2024-01-09 [1] RSPM (R 4.3.0) +## htmltools 0.5.7 2023-11-03 [1] RSPM (R 4.3.0) +## htmlwidgets 1.6.4 2023-12-06 [1] RSPM (R 4.3.0) +## httpuv 1.6.14 2024-01-26 [1] RSPM (R 4.3.0) +## jquerylib 0.1.4 2021-04-26 [1] RSPM (R 4.3.0) +## jsonlite 2.0.0 2025-03-27 [1] CRAN (R 4.3.2) +## knitr 1.50 2025-03-16 [1] CRAN (R 4.3.2) +## later 1.3.2 2023-12-06 [1] RSPM (R 4.3.0) +## lifecycle 1.0.4 2023-11-07 [1] RSPM (R 4.3.0) +## magrittr 2.0.3 2022-03-30 [1] RSPM (R 4.3.0) +## memoise 2.0.1 2021-11-26 [1] RSPM (R 4.3.0) +## mime 0.12 2021-09-28 [1] RSPM (R 4.3.0) +## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.3.0) +## pkgbuild 1.4.3 2023-12-10 [1] RSPM (R 4.3.0) +## pkgload 1.4.1 2025-09-23 [1] CRAN (R 4.3.2) +## profvis 0.3.8 2023-05-02 [1] RSPM (R 4.3.0) +## promises 1.2.1 2023-08-10 [1] RSPM (R 4.3.0) +## purrr 1.0.2 2023-08-10 [1] RSPM (R 4.3.0) +## R6 2.6.1 2025-02-15 [1] CRAN (R 4.3.2) +## Rcpp 1.0.12 2024-01-09 [1] RSPM (R 4.3.0) +## remotes 2.4.2.1 2023-07-18 [1] RSPM (R 4.3.0) +## rlang 1.1.6 2025-04-11 [1] CRAN (R 4.3.2) +## rmarkdown 2.25 2023-09-18 [1] RSPM (R 4.3.0) +## sass 0.4.8 2023-12-06 [1] RSPM (R 4.3.0) +## sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.3.0) +## shiny 1.8.0 2023-11-17 [1] RSPM (R 4.3.0) +## stringi 1.8.3 2023-12-11 [1] RSPM (R 4.3.0) +## stringr 1.5.1 2023-11-14 [1] RSPM (R 4.3.0) +## urlchecker 1.0.1 2021-11-30 [1] RSPM (R 4.3.0) +## usethis 2.2.3 2024-02-19 [1] RSPM (R 4.3.0) +## vctrs 0.6.5 2023-12-01 [1] RSPM (R 4.3.0) +## xfun 0.55 2025-12-16 [1] CRAN (R 4.3.2) +## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.3.0) +## yaml 2.3.12 2025-12-10 [1] CRAN (R 4.3.2) +## +## [1] /usr/local/lib/R/site-library +## [2] /usr/local/lib/R/library +## +## ────────────────────────────────────────────────────────────────────────────── +``` + + diff --git a/docs/References.md b/docs/References.md new file mode 100644 index 0000000..48143a7 --- /dev/null +++ b/docs/References.md @@ -0,0 +1,3 @@ + +# References {-} + diff --git a/docs/about-the-authors.html b/docs/about-the-authors.html new file mode 100644 index 0000000..78a94b2 --- /dev/null +++ b/docs/about-the-authors.html @@ -0,0 +1,488 @@ + + + + + + + About the Authors | Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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About the Authors

+

These credits are based on our course contributors table guidelines.

+

  + 

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CreditsNames
Pedagogy
Lead Content Instructor(s)FirstName LastName
Lecturer(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involvedDelivered the course in some way - video or audio
Content Author(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involvedIf any other authors besides lead instructor
Content Contributor(s) (include section name/link in parentheses) - make new line if more than one section involvedJennifer Kelleher, Ph.D.1*; Abigail S. Robbertz, Ph.D.
Content Editor(s)/Reviewer(s)Julia K. Herriott, B.S.
Content Director(s)Helped guide the content direction
Content Consultants (include chapter name/link in parentheses or word “General”) - make new line if more than one chapter involvedGave high level advice on content
AcknowledgmentsGave small assistance to content but not to the level of consulting
Production
Content Publisher(s)Helped with publishing platform
Content Publishing Reviewer(s)Reviewed overall content and aesthetics on publishing platform
Technical
Course Publishing Engineer(s)Helped with the code for the technical aspects related to the specific course generation
Template Publishing EngineersCandace Savonen, Carrie Wright
Publishing Maintenance EngineerCandace Savonen
Technical Publishing StylistsCarrie Wright, Candace Savonen
Package Developers (Leanbuild)John Muschelli, Candace Savonen, Carrie Wright
Art and Design
Illustrator(s)Created graphics for the course
Figure Artist(s)Created figures/plots for course
Videographer(s)Filmed videos
Videography Editor(s)Edited film
Audiographer(s)Recorded audio
Audiography Editor(s)Edited audio recordings
Funding
Funder(s)Institution/individual who funded course including grant number
Funding StaffStaff members who help with funding
+

 

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+## ──────────────────────────────────────────────────────────────────────────────
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+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
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+ + + + + + + + + + + + + diff --git a/docs/appendix-i-ontologies-vocabularies-taxonomies-and-standards.html b/docs/appendix-i-ontologies-vocabularies-taxonomies-and-standards.html new file mode 100644 index 0000000..7b666e3 --- /dev/null +++ b/docs/appendix-i-ontologies-vocabularies-taxonomies-and-standards.html @@ -0,0 +1,305 @@ + + + + + + + Chapter 6 Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards | Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Chapter 6 Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards

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6.1 SNOMED

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6.2 ICD10

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6.3 RxNorm

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6.4 LOINC

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6.5 Emerging standards such as FHIR

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6.6 OMOP

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+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
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d,e=m(c.createElement(b)).appendTo(c.body),f=a.getDefaultComputedStyle&&(d=a.getDefaultComputedStyle(e[0]))?d.display:m.css(e[0],"display");return e.detach(),f}function Fa(a){var b=y,c=Da[a];return c||(c=Ea(a,b),"none"!==c&&c||(Ca=(Ca||m(" +

OR this works:

+ + + + +
+

2.1.6 Citation examples

+

We can put citations at the end of a sentence like this (Allaire et al. 2021). +Or multiple citations Xie, Allaire, and Grolemund (2018).

+

but they need a ; separator (Allaire et al. 2021; Xie, Allaire, and Grolemund 2018).

+

In text, we can put citations like this Allaire et al. (2021).

+
+ + + +

References

+
+
+Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2021. Rmarkdown: Dynamic Documents for r. https://github.com/rstudio/rmarkdown. +
+
+Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown. +
+
+
+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
+
+ + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/data-analysis-of-clinical-language-data.html b/docs/data-analysis-of-clinical-language-data.html new file mode 100644 index 0000000..41ab501 --- /dev/null +++ b/docs/data-analysis-of-clinical-language-data.html @@ -0,0 +1,298 @@ + + + + + + + Chapter 6 Data Analysis of Clinical Language Data | Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+ +
+
+ + +
+
+ +
+ + + + + + + + + +
+ +
+
+

Chapter 6 Data Analysis of Clinical Language Data

+
+

6.1 Learning Objectives

+
+
+

6.2 Overview of clinical data analysis, language specific

+
+
+

6.3 Natural Language Processing (NLP)

+
+
+

6.4 Unified medical language systems (UMLS)

+
+
+

6.5 Controlled unclassified information (CUIs)

+
+
+

6.6 LLMs

+
+
+

6.7 Showcase of ITCR analysis tools

+
+
+

6.8 Conclusion

+ +
+
+
+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
+
+
+ +
+
+
+ + +
+
+ + + + + + + + + + + + + diff --git a/docs/data-analysis-of-clinical-measures-data.html b/docs/data-analysis-of-clinical-measures-data.html new file mode 100644 index 0000000..cfd2a37 --- /dev/null +++ b/docs/data-analysis-of-clinical-measures-data.html @@ -0,0 +1,304 @@ + + + + + + + Chapter 7 Data Analysis of Clinical Measures Data | Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ +
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+

Chapter 7 Data Analysis of Clinical Measures Data

+
+

7.1 Learning Objectives

+
+
+

7.2 Overview of clinical data analysis, measures specific

+
+
+

7.3 Data cleaning/handling

+
+
+

7.4 Longitudinal and repeated event data analysis

+
+
+

7.5 Statistical Methods

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+

7.6 Tools for both structured and unstructured data

+
+
+

7.7 Showcase of ITCR analysis tools

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+
+

7.8 Patient-facing informatics

+
+
+

7.9 Common data models & national research initiatives

+
+
+

7.10 Conclusion

+ +
+
+
+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
+
+
+ +
+
+
+ + +
+
+ + + + + + + + + + + + + diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..608ad07 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,292 @@ + + + + + + + Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ +
+ + + + + + + + + +
+ +
+ +
+

About this Course

+

This course is part of a series of courses created for the Informatics Technology for Cancer Research (ITCR) Training Network (ITN). The ITN is a collaborative effort of researchers around the United States that supports cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: National Cancer Institute (NCI) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our website at itcrtraining.org for more information. Except where otherwise indicated, the contents of this course are available for use under the Creative Commons Attribution 4.0 license. You are free to adapt and share the work, but you must give appropriate credit, provide a link to the license, and indicate if changes were made. Sample attribution: Cancer Data Management by Fred Hutchinson Data Science Lab (CC-BY 4.0). You can download the illustrations by clicking on this link for our slides.

+ +
+
+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
+
+
+ +
+
+
+ + +
+
+ + + + + + + + + + + + + diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..c1f0590 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,18 @@ +--- +title: "Informatics Technology for Cancer Research (ITCR): Clinical Data Management " +date: "May, 2026" +site: bookdown::bookdown_site +documentclass: book +bibliography: [book.bib, packages.bib] +biblio-style: apalike +link-citations: yes +description: "Description about Course/Book." +favicon: assets/ITN_favicon.ico +--- + + + + +# About this Course {-} + +This course is part of a series of courses created for the [Informatics Technology for Cancer Research (ITCR)](https://www.cancer.gov/about-nci/organization/cssi/research/itcr) Training Network (ITN). The ITN is a collaborative effort of researchers around the United States that supports cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: [National Cancer Institute (NCI)](https://www.cancer.gov/) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our [website](https://www.itcrtraining.org) at itcrtraining.org for more information. Except where otherwise indicated, the contents of this course are available for use under the Creative Commons Attribution 4.0 license. You are free to adapt and share the work, but you must give appropriate credit, provide a link to the license, and indicate if changes were made. Sample attribution: Cancer Data Management by Fred Hutchinson Data Science Lab (CC-BY 4.0). You can download the illustrations by clicking on this [link for our slides](https://docs.google.com/presentation/d/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ/edit?usp=sharing). diff --git a/docs/introduction-1.html b/docs/introduction-1.html new file mode 100644 index 0000000..aaae706 --- /dev/null +++ b/docs/introduction-1.html @@ -0,0 +1,299 @@ + + + + + + + Chapter 2 Introduction | Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ + +
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+ +
+ + + + + + + + + +
+ +
+
+

Chapter 2 Introduction

+
+

2.1 How clinical data differs from other data types

+

Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data, clinical data is often heterogeneous, integrating quantitative measurements, categorical data, subjective narratives from patient notes, and objective observations from doctor notes and even possible image analysis. Free text adds a layer of complexity with unstructured information, reflecting subjective patient experiences or qualitative insights from healthcare professionals. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data’s unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare.

+
+
+

2.2 Major clinical data types

+
+
+

2.3 Specific types of clinical data

+
+
+

2.4 How to acquire clinical data

+
+
+

2.5 Description of data

+
+

2.5.1 File types

+
+
+

2.5.2 Metadata

+
+
+
+

2.6 What questions can be asked with clinical data?

+ +
+
+
+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
+
+
+ +
+
+
+ + +
+
+ + + + + + + + + + + + + diff --git a/docs/introduction.html b/docs/introduction.html new file mode 100644 index 0000000..0cde38f --- /dev/null +++ b/docs/introduction.html @@ -0,0 +1,303 @@ + + + + + + + Chapter 1 Introduction | Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ + + + + + + + + +
+ +
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+

Chapter 1 Introduction

+

Clinical Data Management, Written and assembled by: Kate Isaac & Carrie Wright, Contributors: Christopher Battiston, David Hanauer, Guilherme Del Fiol, Harry Hochheiser, Jennifer Kelleher, Abby Robbertz, & Meghan McGrady

+
+

1.1 Motivation

+

This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines.

+

This course is intended to provide an overview of the different kinds of data commonly used in clinical research, the questions that can typically be asked with such data, as well as guidance for how to manage this data.

+

For individuals who: Have clinical data and don’t know what to do with it. Want a basic overview of what the clinical data represent and what questions can be asked.Need to know the basics of how to manage clinical data properly

+
+
+

1.2 Topics Covered

+

Concepts discussed in Clinical Data course: What does your clinical data type represent? How do you handle the clinical data securely and ethically? What types of analysis questions can one ask when it comes to different kinds of clinical data? Find resources, tools and tutorials to help you process and work with your data

+
+
+

1.3 Curriculum

+

The course will cover key underlying principles and concepts in ethical data handling.

+

Overall Course Learning Objectives. This course will demonstrate how to: List common sources and types of clinical data, Explain the unique aspects related to clinical data, Understand uses and the questions that can be asked with the major types of clinical data,Recognize major tools and methods for managing clinical data ethically and securely

+ +
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+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
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+ + +
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+ + + + + + + + + + + + + diff --git a/docs/libs/anchor-sections-1.1.0/anchor-sections-hash.css b/docs/libs/anchor-sections-1.1.0/anchor-sections-hash.css new file mode 100644 index 0000000..b563ec9 --- /dev/null +++ b/docs/libs/anchor-sections-1.1.0/anchor-sections-hash.css @@ -0,0 +1,2 @@ +/* Styles for section anchors */ +a.anchor-section::before {content: '#';font-size: 80%;} diff --git a/docs/libs/anchor-sections-1.1.0/anchor-sections.css b/docs/libs/anchor-sections-1.1.0/anchor-sections.css new file mode 100644 index 0000000..041905f --- /dev/null +++ b/docs/libs/anchor-sections-1.1.0/anchor-sections.css @@ -0,0 +1,4 @@ +/* Styles for section anchors */ +a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;} +.hasAnchor:hover a.anchor-section {visibility: visible;} +ul > li > .anchor-section {display: none;} diff --git a/docs/libs/anchor-sections-1.1.0/anchor-sections.js b/docs/libs/anchor-sections-1.1.0/anchor-sections.js new file mode 100644 index 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div.proposition, div.conjecture { + font-style: italic; +} +span.theorem, span.lemma, span.corollary, span.proposition, span.conjecture { + font-style: normal; +} +div.proof>*:last-child:after { + content: "\25a2"; + float: right; +} +.header-section-number { + padding-right: .5em; +} +#header .multi-author { + margin: 0.5em 0 -0.5em 0; +} +#header .date { + margin-top: 1.5em; +} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css b/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css new file mode 100644 index 0000000..6844a70 --- /dev/null +++ b/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css @@ -0,0 +1,18 @@ +div.sourceCode { + position: relative; +} + +.copy-to-clipboard-button { + position: absolute; + right: 0; + top: 0; + visibility: hidden; +} + +.copy-to-clipboard-button:focus { + outline: 0; +} + +div.sourceCode:hover > .copy-to-clipboard-button { + visibility: visible; +} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css b/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css new file mode 100644 index 0000000..3fa6f35 --- /dev/null +++ b/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css @@ -0,0 +1,303 @@ +/* + * Theme 1 + */ +.color-theme-1 .dropdown-menu { + background-color: #111111; + border-color: #7e888b; +} +.color-theme-1 .dropdown-menu .dropdown-caret .caret-inner { + border-bottom: 9px solid #111111; +} +.color-theme-1 .dropdown-menu .buttons { + border-color: #7e888b; +} +.color-theme-1 .dropdown-menu .button { + color: #afa790; +} +.color-theme-1 .dropdown-menu .button:hover { + color: #73553c; +} +/* + * Theme 2 + */ +.color-theme-2 .dropdown-menu { + background-color: #2d3143; + border-color: #272a3a; +} +.color-theme-2 .dropdown-menu .dropdown-caret .caret-inner { + border-bottom: 9px solid #2d3143; +} +.color-theme-2 .dropdown-menu .buttons { + border-color: #272a3a; +} +.color-theme-2 .dropdown-menu .button { + color: #62677f; +} +.color-theme-2 .dropdown-menu .button:hover { + color: #f4f4f5; +} +.book .book-header .font-settings .font-enlarge { + line-height: 30px; + font-size: 1.4em; +} +.book .book-header .font-settings .font-reduce { + line-height: 30px; + font-size: 1em; +} + +/* sidebar transition background */ +div.book.color-theme-1 { + background: #f3eacb; +} +.book.color-theme-1 .book-body { + color: #704214; + background: #f3eacb; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section { + background: #f3eacb; +} + +/* sidebar transition background */ +div.book.color-theme-2 { + background: #1c1f2b; +} + +.book.color-theme-2 .book-body { + color: #bdcadb; + background: #1c1f2b; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section { + background: #1c1f2b; +} +.book.font-size-0 .book-body .page-inner section { + font-size: 1.2rem; +} +.book.font-size-1 .book-body .page-inner section { + font-size: 1.4rem; +} +.book.font-size-2 .book-body .page-inner section { + font-size: 1.6rem; +} +.book.font-size-3 .book-body .page-inner section { + font-size: 2.2rem; +} +.book.font-size-4 .book-body .page-inner section { + font-size: 4rem; +} +.book.font-family-0 { + font-family: Georgia, serif; +} +.book.font-family-1 { + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal { + color: #704214; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal a { + color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h1, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h2, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h3, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h4, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h5, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h6 { + color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h1, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h2 { + border-color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h6 { + color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal hr { + background-color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal blockquote { + border-color: #c4b29f; + opacity: 0.9; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code { + background: #fdf6e3; + color: #657b83; + border-color: #f8df9c; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal .highlight { + background-color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table th, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table td { + border-color: #f5d06c; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table tr { + color: inherit; + background-color: #fdf6e3; + border-color: #444444; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table tr:nth-child(2n) { + background-color: #fbeecb; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal { + color: #bdcadb; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal a { + color: #3eb1d0; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h1, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h2, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h3, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h4, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h5, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h6 { + color: #fffffa; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h1, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h2 { + border-color: #373b4e; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h6 { + color: #373b4e; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal hr { + background-color: #373b4e; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal blockquote { + border-color: #373b4e; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal pre, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal code { + color: #9dbed8; + background: #2d3143; + border-color: #2d3143; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal .highlight { + background-color: #282a39; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table th, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table td { + border-color: #3b3f54; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table tr { + color: #b6c2d2; + background-color: #2d3143; + border-color: #3b3f54; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table tr:nth-child(2n) { + background-color: #35394b; +} +.book.color-theme-1 .book-header { + color: #afa790; + background: transparent; +} +.book.color-theme-1 .book-header .btn { + color: #afa790; +} +.book.color-theme-1 .book-header .btn:hover { + color: #73553c; + background: none; +} +.book.color-theme-1 .book-header h1 { + color: #704214; +} +.book.color-theme-2 .book-header { + color: #7e888b; + background: transparent; +} +.book.color-theme-2 .book-header .btn { + color: #3b3f54; +} +.book.color-theme-2 .book-header .btn:hover { + color: #fffff5; + background: none; +} +.book.color-theme-2 .book-header h1 { + color: #bdcadb; +} +.book.color-theme-1 .book-body .navigation { + color: #afa790; +} +.book.color-theme-1 .book-body .navigation:hover { + color: #73553c; +} +.book.color-theme-2 .book-body .navigation { + color: #383f52; +} +.book.color-theme-2 .book-body .navigation:hover { + color: #fffff5; +} +/* + * Theme 1 + */ +.book.color-theme-1 .book-summary { + color: #afa790; + background: #111111; + border-right: 1px solid rgba(0, 0, 0, 0.07); +} +.book.color-theme-1 .book-summary .book-search { + background: transparent; +} +.book.color-theme-1 .book-summary .book-search input, +.book.color-theme-1 .book-summary .book-search input:focus { + border: 1px solid transparent; +} +.book.color-theme-1 .book-summary ul.summary li.divider { + background: #7e888b; + box-shadow: none; +} +.book.color-theme-1 .book-summary ul.summary li i.fa-check { + color: #33cc33; +} +.book.color-theme-1 .book-summary ul.summary li.done > a { + color: #877f6a; +} +.book.color-theme-1 .book-summary ul.summary li a, +.book.color-theme-1 .book-summary ul.summary li span { + color: #877f6a; + background: transparent; + font-weight: normal; +} +.book.color-theme-1 .book-summary ul.summary li.active > a, +.book.color-theme-1 .book-summary ul.summary li a:hover { + color: #704214; + background: transparent; + font-weight: normal; +} +/* + * Theme 2 + */ +.book.color-theme-2 .book-summary { + color: #bcc1d2; + background: #2d3143; + border-right: none; +} +.book.color-theme-2 .book-summary .book-search { + background: transparent; +} +.book.color-theme-2 .book-summary .book-search input, +.book.color-theme-2 .book-summary .book-search input:focus { + border: 1px solid transparent; +} +.book.color-theme-2 .book-summary ul.summary li.divider { + background: #272a3a; + box-shadow: none; +} +.book.color-theme-2 .book-summary ul.summary li i.fa-check { + color: #33cc33; +} +.book.color-theme-2 .book-summary ul.summary li.done > a { + color: #62687f; +} +.book.color-theme-2 .book-summary ul.summary li a, +.book.color-theme-2 .book-summary ul.summary li span { + color: #c1c6d7; + background: transparent; + font-weight: 600; +} +.book.color-theme-2 .book-summary ul.summary li.active > a, +.book.color-theme-2 .book-summary ul.summary li a:hover { + color: #f4f4f5; + background: #252737; + font-weight: 600; +} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-highlight.css b/docs/libs/gitbook-2.6.7/css/plugin-highlight.css new file mode 100644 index 0000000..02c0189 --- /dev/null +++ b/docs/libs/gitbook-2.6.7/css/plugin-highlight.css @@ -0,0 +1,426 @@ +.book .book-body .page-wrapper .page-inner section.normal pre, +.book .book-body .page-wrapper .page-inner section.normal code { + /* http://jmblog.github.com/color-themes-for-google-code-highlightjs */ + /* Tomorrow Comment */ + /* Tomorrow Red */ + /* Tomorrow Orange */ + /* Tomorrow Yellow */ + /* Tomorrow Green */ + /* Tomorrow Aqua */ + /* Tomorrow Blue */ + /* Tomorrow Purple */ +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-comment, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-comment, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-title { + color: #8e908c; +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-variable, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-variable, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-attribute, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-attribute, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-tag, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-tag, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-regexp, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-regexp, +.book .book-body .page-wrapper .page-inner section.normal pre .ruby .hljs-constant, +.book .book-body .page-wrapper .page-inner section.normal code .ruby .hljs-constant, +.book .book-body 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modifiers=[];if(e.shiftKey){modifiers.push("shift")}if(e.altKey){modifiers.push("alt")}if(e.ctrlKey){modifiers.push("ctrl")}if(e.metaKey){modifiers.push("meta")}return modifiers}function _preventDefault(e){if(e.preventDefault){e.preventDefault();return}e.returnValue=false}function _stopPropagation(e){if(e.stopPropagation){e.stopPropagation();return}e.cancelBubble=true}function _isModifier(key){return key=="shift"||key=="ctrl"||key=="alt"||key=="meta"}function _getReverseMap(){if(!_REVERSE_MAP){_REVERSE_MAP={};for(var key in _MAP){if(key>95&&key<112){continue}if(_MAP.hasOwnProperty(key)){_REVERSE_MAP[_MAP[key]]=key}}}return _REVERSE_MAP}function _pickBestAction(key,modifiers,action){if(!action){action=_getReverseMap()[key]?"keydown":"keypress"}if(action=="keypress"&&modifiers.length){action="keydown"}return action}function _keysFromString(combination){if(combination==="+"){return["+"]}combination=combination.replace(/\+{2}/g,"+plus");return combination.split("+")}function _getKeyInfo(combination,action){var keys;var key;var i;var modifiers=[];keys=_keysFromString(combination);for(i=0;i1){_bindSequence(combination,sequence,callback,action);return}info=_getKeyInfo(combination,action);self._callbacks[info.key]=self._callbacks[info.key]||[];_getMatches(info.key,info.modifiers,{type:info.action},sequenceName,combination,level);self._callbacks[info.key][sequenceName?"unshift":"push"]({callback:callback,modifiers:info.modifiers,action:info.action,seq:sequenceName,level:level,combo:combination})}self._bindMultiple=function(combinations,callback,action){for(var i=0;i-1){return false}if(_belongsTo(element,self.target)){return false}return element.tagName=="INPUT"||element.tagName=="SELECT"||element.tagName=="TEXTAREA"||element.isContentEditable};Mousetrap.prototype.handleKey=function(){var self=this;return self._handleKey.apply(self,arguments)};Mousetrap.init=function(){var documentMousetrap=Mousetrap(document);for(var method in documentMousetrap){if(method.charAt(0)!=="_"){Mousetrap[method]=function(method){return function(){return documentMousetrap[method].apply(documentMousetrap,arguments)}}(method)}}};Mousetrap.init();window.Mousetrap=Mousetrap;if(typeof module!=="undefined"&&module.exports){module.exports=Mousetrap}if(typeof define==="function"&&define.amd){define(function(){return Mousetrap})}})(window,document)},{}],4:[function(require,module,exports){(function(process){function normalizeArray(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up--;up){parts.unshift("..")}}return parts}var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;var splitPath=function(filename){return splitPathRe.exec(filename).slice(1)};exports.resolve=function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:process.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){continue}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=normalizeArray(filter(resolvedPath.split("/"),function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."};exports.normalize=function(path){var isAbsolute=exports.isAbsolute(path),trailingSlash=substr(path,-1)==="/";path=normalizeArray(filter(path.split("/"),function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path};exports.isAbsolute=function(path){return path.charAt(0)==="/"};exports.join=function(){var paths=Array.prototype.slice.call(arguments,0);return exports.normalize(filter(paths,function(p,index){if(typeof p!=="string"){throw new TypeError("Arguments to path.join must be strings")}return p}).join("/"))};exports.relative=function(from,to){from=exports.resolve(from).substr(1);to=exports.resolve(to).substr(1);function trim(arr){var start=0;for(;start=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i1){for(var i=1;i= 0x80 (not a basic code point)","invalid-input":"Invalid input"},baseMinusTMin=base-tMin,floor=Math.floor,stringFromCharCode=String.fromCharCode,key;function error(type){throw RangeError(errors[type])}function map(array,fn){var length=array.length;var result=[];while(length--){result[length]=fn(array[length])}return result}function mapDomain(string,fn){var parts=string.split("@");var result="";if(parts.length>1){result=parts[0]+"@";string=parts[1]}string=string.replace(regexSeparators,".");var labels=string.split(".");var encoded=map(labels,fn).join(".");return result+encoded}function ucs2decode(string){var output=[],counter=0,length=string.length,value,extra;while(counter=55296&&value<=56319&&counter65535){value-=65536;output+=stringFromCharCode(value>>>10&1023|55296);value=56320|value&1023}output+=stringFromCharCode(value);return output}).join("")}function basicToDigit(codePoint){if(codePoint-48<10){return codePoint-22}if(codePoint-65<26){return codePoint-65}if(codePoint-97<26){return codePoint-97}return base}function digitToBasic(digit,flag){return digit+22+75*(digit<26)-((flag!=0)<<5)}function adapt(delta,numPoints,firstTime){var k=0;delta=firstTime?floor(delta/damp):delta>>1;delta+=floor(delta/numPoints);for(;delta>baseMinusTMin*tMax>>1;k+=base){delta=floor(delta/baseMinusTMin)}return floor(k+(baseMinusTMin+1)*delta/(delta+skew))}function decode(input){var output=[],inputLength=input.length,out,i=0,n=initialN,bias=initialBias,basic,j,index,oldi,w,k,digit,t,baseMinusT;basic=input.lastIndexOf(delimiter);if(basic<0){basic=0}for(j=0;j=128){error("not-basic")}output.push(input.charCodeAt(j))}for(index=basic>0?basic+1:0;index=inputLength){error("invalid-input")}digit=basicToDigit(input.charCodeAt(index++));if(digit>=base||digit>floor((maxInt-i)/w)){error("overflow")}i+=digit*w;t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(digitfloor(maxInt/baseMinusT)){error("overflow")}w*=baseMinusT}out=output.length+1;bias=adapt(i-oldi,out,oldi==0);if(floor(i/out)>maxInt-n){error("overflow")}n+=floor(i/out);i%=out;output.splice(i++,0,n)}return ucs2encode(output)}function encode(input){var n,delta,handledCPCount,basicLength,bias,j,m,q,k,t,currentValue,output=[],inputLength,handledCPCountPlusOne,baseMinusT,qMinusT;input=ucs2decode(input);inputLength=input.length;n=initialN;delta=0;bias=initialBias;for(j=0;j=n&¤tValuefloor((maxInt-delta)/handledCPCountPlusOne)){error("overflow")}delta+=(m-n)*handledCPCountPlusOne;n=m;for(j=0;jmaxInt){error("overflow")}if(currentValue==n){for(q=delta,k=base;;k+=base){t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(q0&&len>maxKeys){len=maxKeys}for(var i=0;i=0){kstr=x.substr(0,idx);vstr=x.substr(idx+1)}else{kstr=x;vstr=""}k=decodeURIComponent(kstr);v=decodeURIComponent(vstr);if(!hasOwnProperty(obj,k)){obj[k]=v}else if(isArray(obj[k])){obj[k].push(v)}else{obj[k]=[obj[k],v]}}return obj};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"}},{}],8:[function(require,module,exports){"use strict";var stringifyPrimitive=function(v){switch(typeof v){case"string":return v;case"boolean":return v?"true":"false";case"number":return isFinite(v)?v:"";default:return""}};module.exports=function(obj,sep,eq,name){sep=sep||"&";eq=eq||"=";if(obj===null){obj=undefined}if(typeof obj==="object"){return map(objectKeys(obj),function(k){var ks=encodeURIComponent(stringifyPrimitive(k))+eq;if(isArray(obj[k])){return map(obj[k],function(v){return ks+encodeURIComponent(stringifyPrimitive(v))}).join(sep)}else{return ks+encodeURIComponent(stringifyPrimitive(obj[k]))}}).join(sep)}if(!name)return"";return encodeURIComponent(stringifyPrimitive(name))+eq+encodeURIComponent(stringifyPrimitive(obj))};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"};function map(xs,f){if(xs.map)return xs.map(f);var res=[];for(var i=0;i",'"',"`"," ","\r","\n","\t"],unwise=["{","}","|","\\","^","`"].concat(delims),autoEscape=["'"].concat(unwise),nonHostChars=["%","/","?",";","#"].concat(autoEscape),hostEndingChars=["/","?","#"],hostnameMaxLen=255,hostnamePartPattern=/^[a-z0-9A-Z_-]{0,63}$/,hostnamePartStart=/^([a-z0-9A-Z_-]{0,63})(.*)$/,unsafeProtocol={javascript:true,"javascript:":true},hostlessProtocol={javascript:true,"javascript:":true},slashedProtocol={http:true,https:true,ftp:true,gopher:true,file:true,"http:":true,"https:":true,"ftp:":true,"gopher:":true,"file:":true},querystring=require("querystring");function urlParse(url,parseQueryString,slashesDenoteHost){if(url&&isObject(url)&&url instanceof Url)return url;var u=new Url;u.parse(url,parseQueryString,slashesDenoteHost);return u}Url.prototype.parse=function(url,parseQueryString,slashesDenoteHost){if(!isString(url)){throw new TypeError("Parameter 'url' must be a string, not "+typeof url)}var rest=url;rest=rest.trim();var proto=protocolPattern.exec(rest);if(proto){proto=proto[0];var lowerProto=proto.toLowerCase();this.protocol=lowerProto;rest=rest.substr(proto.length)}if(slashesDenoteHost||proto||rest.match(/^\/\/[^@\/]+@[^@\/]+/)){var slashes=rest.substr(0,2)==="//";if(slashes&&!(proto&&hostlessProtocol[proto])){rest=rest.substr(2);this.slashes=true}}if(!hostlessProtocol[proto]&&(slashes||proto&&!slashedProtocol[proto])){var hostEnd=-1;for(var i=0;i127){newpart+="x"}else{newpart+=part[j]}}if(!newpart.match(hostnamePartPattern)){var validParts=hostparts.slice(0,i);var notHost=hostparts.slice(i+1);var bit=part.match(hostnamePartStart);if(bit){validParts.push(bit[1]);notHost.unshift(bit[2])}if(notHost.length){rest="/"+notHost.join(".")+rest}this.hostname=validParts.join(".");break}}}}if(this.hostname.length>hostnameMaxLen){this.hostname=""}else{this.hostname=this.hostname.toLowerCase()}if(!ipv6Hostname){var domainArray=this.hostname.split(".");var newOut=[];for(var i=0;i0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}result.search=relative.search;result.query=relative.query;if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.href=result.format();return result}if(!srcPath.length){result.pathname=null;if(result.search){result.path="/"+result.search}else{result.path=null}result.href=result.format();return result}var last=srcPath.slice(-1)[0];var hasTrailingSlash=(result.host||relative.host)&&(last==="."||last==="..")||last==="";var up=0;for(var i=srcPath.length;i>=0;i--){last=srcPath[i];if(last=="."){srcPath.splice(i,1)}else if(last===".."){srcPath.splice(i,1);up++}else if(up){srcPath.splice(i,1);up--}}if(!mustEndAbs&&!removeAllDots){for(;up--;up){srcPath.unshift("..")}}if(mustEndAbs&&srcPath[0]!==""&&(!srcPath[0]||srcPath[0].charAt(0)!=="/")){srcPath.unshift("")}if(hasTrailingSlash&&srcPath.join("/").substr(-1)!=="/"){srcPath.push("")}var isAbsolute=srcPath[0]===""||srcPath[0]&&srcPath[0].charAt(0)==="/";if(psychotic){result.hostname=result.host=isAbsolute?"":srcPath.length?srcPath.shift():"";var authInHost=result.host&&result.host.indexOf("@")>0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}mustEndAbs=mustEndAbs||result.host&&srcPath.length;if(mustEndAbs&&!isAbsolute){srcPath.unshift("")}if(!srcPath.length){result.pathname=null;result.path=null}else{result.pathname=srcPath.join("/")}if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.auth=relative.auth||result.auth;result.slashes=result.slashes||relative.slashes;result.href=result.format();return result};Url.prototype.parseHost=function(){var host=this.host;var port=portPattern.exec(host);if(port){port=port[0];if(port!==":"){this.port=port.substr(1)}host=host.substr(0,host.length-port.length)}if(host)this.hostname=host};function isString(arg){return typeof arg==="string"}function isObject(arg){return typeof arg==="object"&&arg!==null}function isNull(arg){return arg===null}function isNullOrUndefined(arg){return arg==null}},{punycode:6,querystring:9}],11:[function(require,module,exports){var $=require("jquery");function toggleDropdown(e){var $dropdown=$(e.currentTarget).parent().find(".dropdown-menu");$dropdown.toggleClass("open");e.stopPropagation();e.preventDefault()}function closeDropdown(e){$(".dropdown-menu").removeClass("open")}function init(){$(document).on("click",".toggle-dropdown",toggleDropdown);$(document).on("click",".dropdown-menu",function(e){e.stopPropagation()});$(document).on("click",closeDropdown)}module.exports={init:init}},{jquery:1}],12:[function(require,module,exports){var $=require("jquery");module.exports=$({})},{jquery:1}],13:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var dropdown=require("./dropdown");var events=require("./events");var state=require("./state");var keyboard=require("./keyboard");var navigation=require("./navigation");var sidebar=require("./sidebar");var toolbar=require("./toolbar");function start(config){sidebar.init();keyboard.init();dropdown.init();navigation.init();toolbar.createButton({index:0,icon:"fa fa-align-justify",label:"Toggle Sidebar",onClick:function(e){e.preventDefault();sidebar.toggle()}});events.trigger("start",config);navigation.notify()}var gitbook={start:start,events:events,state:state,toolbar:toolbar,sidebar:sidebar,storage:storage,keyboard:keyboard};var MODULES={gitbook:gitbook,jquery:$,lodash:_};window.gitbook=gitbook;window.$=$;window.jQuery=$;gitbook.require=function(mods,fn){mods=_.map(mods,function(mod){mod=mod.toLowerCase();if(!MODULES[mod]){throw new Error("GitBook module "+mod+" doesn't exist")}return MODULES[mod]});fn.apply(null,mods)};module.exports={}},{"./dropdown":11,"./events":12,"./keyboard":14,"./navigation":16,"./sidebar":18,"./state":19,"./storage":20,"./toolbar":21,jquery:1,lodash:2}],14:[function(require,module,exports){var Mousetrap=require("mousetrap");var navigation=require("./navigation");var sidebar=require("./sidebar");function bindShortcut(keys,fn){Mousetrap.bind(keys,function(e){fn();return false})}function init(){bindShortcut(["right"],function(e){navigation.goNext()});bindShortcut(["left"],function(e){navigation.goPrev()});bindShortcut(["s"],function(e){sidebar.toggle()})}module.exports={init:init,bind:bindShortcut}},{"./navigation":16,"./sidebar":18,mousetrap:3}],15:[function(require,module,exports){var state=require("./state");function showLoading(p){state.$book.addClass("is-loading");p.always(function(){state.$book.removeClass("is-loading")});return p}module.exports={show:showLoading}},{"./state":19}],16:[function(require,module,exports){var $=require("jquery");var url=require("url");var events=require("./events");var state=require("./state");var loading=require("./loading");var usePushState=typeof history.pushState!=="undefined";function handleNavigation(relativeUrl,push){var uri=url.resolve(window.location.pathname,relativeUrl);notifyPageChange();location.href=relativeUrl;return}function updateNavigationPosition(){var bodyInnerWidth,pageWrapperWidth;bodyInnerWidth=parseInt($(".body-inner").css("width"),10);pageWrapperWidth=parseInt($(".page-wrapper").css("width"),10);$(".navigation-next").css("margin-right",bodyInnerWidth-pageWrapperWidth+"px")}function notifyPageChange(){events.trigger("page.change")}function preparePage(notify){var $bookBody=$(".book-body");var $bookInner=$bookBody.find(".body-inner");var $pageWrapper=$bookInner.find(".page-wrapper");updateNavigationPosition();$bookInner.scrollTop(0);$bookBody.scrollTop(0);if(notify!==false)notifyPageChange()}function isLeftClickEvent(e){return e.button===0}function isModifiedEvent(e){return!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey)}function handlePagination(e){if(isModifiedEvent(e)||!isLeftClickEvent(e)){return}e.stopPropagation();e.preventDefault();var url=$(this).attr("href");if(url)handleNavigation(url,true)}function goNext(){var url=$(".navigation-next").attr("href");if(url)handleNavigation(url,true)}function goPrev(){var url=$(".navigation-prev").attr("href");if(url)handleNavigation(url,true)}function init(){$.ajaxSetup({});if(location.protocol!=="file:"){history.replaceState({path:window.location.href},"")}window.onpopstate=function(event){if(event.state===null){return}return handleNavigation(event.state.path,false)};$(document).on("click",".navigation-prev",handlePagination);$(document).on("click",".navigation-next",handlePagination);$(document).on("click",".summary [data-path] a",handlePagination);$(window).resize(updateNavigationPosition);preparePage(false)}module.exports={init:init,goNext:goNext,goPrev:goPrev,notify:notifyPageChange}},{"./events":12,"./loading":15,"./state":19,jquery:1,url:10}],17:[function(require,module,exports){module.exports={isMobile:function(){return document.body.clientWidth<=600}}},{}],18:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var platform=require("./platform");var state=require("./state");function toggleSidebar(_state,animation){if(state!=null&&isOpen()==_state)return;if(animation==null)animation=true;state.$book.toggleClass("without-animation",!animation);state.$book.toggleClass("with-summary",_state);storage.set("sidebar",isOpen())}function isOpen(){return state.$book.hasClass("with-summary")}function init(){if(platform.isMobile()){toggleSidebar(false,false)}else{toggleSidebar(storage.get("sidebar",true),false)}$(document).on("click",".book-summary li.chapter a",function(e){if(platform.isMobile())toggleSidebar(false,false)})}function filterSummary(paths){var $summary=$(".book-summary");$summary.find("li").each(function(){var path=$(this).data("path");var st=paths==null||_.contains(paths,path);$(this).toggle(st);if(st)$(this).parents("li").show()})}module.exports={init:init,isOpen:isOpen,toggle:toggleSidebar,filter:filterSummary}},{"./platform":17,"./state":19,"./storage":20,jquery:1,lodash:2}],19:[function(require,module,exports){var $=require("jquery");var url=require("url");var path=require("path");var state={};state.update=function(dom){var $book=$(dom.find(".book"));state.$book=$book;state.level=$book.data("level");state.basePath=$book.data("basepath");state.innerLanguage=$book.data("innerlanguage");state.revision=$book.data("revision");state.filepath=$book.data("filepath");state.chapterTitle=$book.data("chapter-title");state.root=url.resolve(location.protocol+"//"+location.host,path.dirname(path.resolve(location.pathname.replace(/\/$/,"/index.html"),state.basePath))).replace(/\/?$/,"/");state.bookRoot=state.innerLanguage?url.resolve(state.root,".."):state.root};state.update($);module.exports=state},{jquery:1,path:4,url:10}],20:[function(require,module,exports){var baseKey="";module.exports={setBaseKey:function(key){baseKey=key},set:function(key,value){key=baseKey+":"+key;try{sessionStorage[key]=JSON.stringify(value)}catch(e){}},get:function(key,def){key=baseKey+":"+key;if(sessionStorage[key]===undefined)return def;try{var v=JSON.parse(sessionStorage[key]);return v==null?def:v}catch(err){return sessionStorage[key]||def}},remove:function(key){key=baseKey+":"+key;sessionStorage.removeItem(key)}}},{}],21:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var events=require("./events");var buttons=[];function insertAt(parent,selector,index,element){var lastIndex=parent.children(selector).length;if(index<0){index=Math.max(0,lastIndex+1+index)}parent.append(element);if(index",{class:"dropdown-menu",html:'

'});if(_.isString(dropdown)){$menu.append(dropdown)}else{var groups=_.map(dropdown,function(group){if(_.isArray(group))return group;else return[group]});_.each(groups,function(group){var $group=$("
",{class:"buttons"});var sizeClass="size-"+group.length;_.each(group,function(btn){btn=_.defaults(btn||{},{text:"",className:"",onClick:defaultOnClick});var $btn=$("'; + var clipboard; + + gitbook.events.bind("page.change", function() { + + if (!ClipboardJS.isSupported()) return; + + // the page.change event is thrown twice: before and after the page changes + if (clipboard) { + // clipboard is already defined but we are on the same page + if (clipboard._prevPage === window.location.pathname) return; + // clipboard is already defined and url path change + // we can deduct that we are before page changes + clipboard.destroy(); // destroy the previous events listeners + clipboard = undefined; // reset the clipboard object + return; + } + + $(copyButton).prependTo("div.sourceCode"); + + clipboard = new ClipboardJS(".copy-to-clipboard-button", { + text: function(trigger) { + return trigger.parentNode.textContent; + } + }); + + clipboard._prevPage = window.location.pathname + + }); + +}); diff --git a/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js b/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js new file mode 100644 index 0000000..e743148 --- /dev/null +++ b/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js @@ -0,0 +1,175 @@ +gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) { + var fontState; + + var THEMES = { + "white": 0, + "sepia": 1, + "night": 2 + }; + + var FAMILY = { + "serif": 0, + "sans": 1 + }; + + // Save current font settings + function saveFontSettings() { + gitbook.storage.set("fontState", fontState); + update(); + } + + // Increase font size + function enlargeFontSize(e) { + e.preventDefault(); + if (fontState.size >= 4) return; + + fontState.size++; + saveFontSettings(); + }; + + // Decrease font size + function reduceFontSize(e) { + e.preventDefault(); + if (fontState.size <= 0) return; + + fontState.size--; + saveFontSettings(); + }; + + // Change font family + function changeFontFamily(index, e) { + e.preventDefault(); + + fontState.family = index; + saveFontSettings(); + }; + + // Change type of color + function changeColorTheme(index, e) { + e.preventDefault(); + + var $book = $(".book"); + + if (fontState.theme !== 0) + $book.removeClass("color-theme-"+fontState.theme); + + fontState.theme = index; + if (fontState.theme !== 0) + $book.addClass("color-theme-"+fontState.theme); + + saveFontSettings(); + }; + + // Increase or decrease line spacing + function changeSpacing(e, inc = true) { + e.preventDefault(); + inc ? fontState.spacing++ : (fontState.spacing > 10 && fontState.spacing--); + saveFontSettings(); + } + + function update() { + var $book = gitbook.state.$book; + + $(".font-settings .font-family-list li").removeClass("active"); + $(".font-settings .font-family-list li:nth-child("+(fontState.family+1)+")").addClass("active"); + + $book[0].className = $book[0].className.replace(/\bfont-\S+/g, ''); + $book.addClass("font-size-"+fontState.size); + $book.addClass("font-family-"+fontState.family); + + if(fontState.theme !== 0) { + $book[0].className = $book[0].className.replace(/\bcolor-theme-\S+/g, ''); + $book.addClass("color-theme-"+fontState.theme); + } + var lineHeight = fontState.spacing / 10; + $book.find('section').css('line-height', lineHeight); + $('.font-settings .spacing-reduce').prop('disabled', lineHeight <= 1); + }; + + function init(config) { + var $bookBody, $book; + + //Find DOM elements. + $book = gitbook.state.$book; + $bookBody = $book.find(".book-body"); + + // Instantiate font state object + fontState = gitbook.storage.get("fontState", { + size: config.size || 2, + family: FAMILY[config.family || "sans"], + theme: THEMES[config.theme || "white"], + spacing: config.spacing || 17, + }); + + update(); + }; + + + gitbook.events.bind("start", function(e, config) { + var opts = config.fontsettings; + if (!opts) return; + + // Create buttons in toolbar + gitbook.toolbar.createButton({ + icon: 'fa fa-font', + label: 'Font Settings', + className: 'font-settings', + dropdown: [ + [ + { + text: 'A', + className: 'font-reduce', + onClick: reduceFontSize + }, + { + text: 'A', + className: 'font-enlarge', + onClick: enlargeFontSize + } + ], + [ + { + text: 'Serif', + onClick: _.partial(changeFontFamily, 0) + }, + { + text: 'Sans', + onClick: _.partial(changeFontFamily, 1) + } + ], + [ + { + text: 'White', + onClick: _.partial(changeColorTheme, 0) + }, + { + text: 'Sepia', + onClick: _.partial(changeColorTheme, 1) + }, + { + text: 'Night', + onClick: _.partial(changeColorTheme, 2) + } + + ], + [ + { + className: 'spacing-reduce', + text: 'Spacing -', + onClick: e => changeSpacing(e, false) + }, + { + text: 'Spacing +', + onClick: changeSpacing + } + ] + ] + }); + + + // Init current settings + init(opts); + }); +}); + + diff --git a/docs/libs/gitbook-2.6.7/js/plugin-search.js b/docs/libs/gitbook-2.6.7/js/plugin-search.js new file mode 100644 index 0000000..747fcce --- /dev/null +++ b/docs/libs/gitbook-2.6.7/js/plugin-search.js @@ -0,0 +1,270 @@ +gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) { + var index = null; + var fuse = null; + var _search = {engine: 'lunr', opts: {}}; + var $searchInput, $searchLabel, $searchForm; + var $highlighted = [], hi, hiOpts = { className: 'search-highlight' }; + var collapse = false, toc_visible = []; + + function init(config) { + // Instantiate search settings + _search = gitbook.storage.get("search", { + engine: config.search.engine || 'lunr', + opts: config.search.options || {}, + }); + }; + + // Save current search settings + function saveSearchSettings() { + gitbook.storage.set("search", _search); + } + + // Use a specific index + function loadIndex(data) { + // [Yihui] In bookdown, I use a character matrix to store the chapter + // content, and the index is dynamically built on the client side. + // Gitbook prebuilds the index data instead: https://github.com/GitbookIO/plugin-search + // We can certainly do that via R packages V8 and jsonlite, but let's + // see how slow it really is before improving it. On the other hand, + // lunr cannot handle non-English text very well, e.g. the default + // tokenizer cannot deal with Chinese text, so we may want to replace + // lunr with a dumb simple text matching approach. + if (_search.engine === 'lunr') { + index = lunr(function () { + this.ref('url'); + this.field('title', { boost: 10 }); + this.field('body'); + }); + data.map(function(item) { + index.add({ + url: item[0], + title: item[1], + body: item[2] + }); + }); + return; + } + fuse = new Fuse(data.map((_data => { + return { + url: _data[0], + title: _data[1], + body: _data[2] + }; + })), Object.assign( + { + includeScore: true, + threshold: 0.1, + ignoreLocation: true, + keys: ["title", "body"] + }, + _search.opts + )); + } + + // Fetch the search index + function fetchIndex() { + return $.getJSON(gitbook.state.basePath+"/search_index.json") + .then(loadIndex); // [Yihui] we need to use this object later + } + + // Search for a term and return results + function search(q) { + let results = []; + switch (_search.engine) { + case 'fuse': + if (!fuse) return; + results = fuse.search(q).map(function(result) { + var parts = result.item.url.split('#'); + return { + path: parts[0], + hash: parts[1] + }; + }); + break; + case 'lunr': + default: + if (!index) return; + results = _.chain(index.search(q)).map(function(result) { + var parts = result.ref.split("#"); + return { + path: parts[0], + hash: parts[1] + }; + }) + .value(); + } + + // [Yihui] Highlight the search keyword on current page + $highlighted = $('.page-inner') + .unhighlight(hiOpts).highlight(q, hiOpts).find('span.search-highlight'); + scrollToHighlighted(0); + + return results; + } + + // [Yihui] Scroll the chapter body to the i-th highlighted string + function scrollToHighlighted(d) { + var n = $highlighted.length; + hi = hi === undefined ? 0 : hi + d; + // navignate to the previous/next page in the search results if reached the top/bottom + var b = hi < 0; + if (d !== 0 && (b || hi >= n)) { + var path = currentPath(), n2 = toc_visible.length; + if (n2 === 0) return; + for (var i = b ? 0 : n2; (b && i < n2) || (!b && i >= 0); i += b ? 1 : -1) { + if (toc_visible.eq(i).data('path') === path) break; + } + i += b ? -1 : 1; + if (i < 0) i = n2 - 1; + if (i >= n2) i = 0; + var lnk = toc_visible.eq(i).find('a[href$=".html"]'); + if (lnk.length) lnk[0].click(); + return; + } + if (n === 0) return; + var $p = $highlighted.eq(hi); + $p[0].scrollIntoView(); + $highlighted.css('background-color', ''); + // an orange background color on the current item and removed later + $p.css('background-color', 'orange'); + setTimeout(function() { + $p.css('background-color', ''); + }, 2000); + } + + function currentPath() { + var href = window.location.pathname; + href = href.substr(href.lastIndexOf('/') + 1); + return href === '' ? 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It may come in many forms and its sensitive nature requires careful management by researchers. + +## Learning Objectives + +Learning Objectives: 1. Explain why clinical data is unique compared to other types of biomedical research data, 2. Describe the difference between Structured and Unstructured data, 3. List major sources and types of clinical data + + + +## Clinical data is unique + +Clinical data refers to information __collected from patients__ during healthcare delivery, clinical trials, and medical research. Clinical data comes from a wide variety of sources and as such, requires careful consideration when designing, collecting, and analyzing this data. Unlike domains such as Finance or other areas in the sciences which predominantly use structured data with predictable and consistent formats, clinical data is often heterogeneous, integrating many forms of both structured and unstructured data: quantitative measurements, categorical data, genotyping, images, subjective narratives from patient notes, and objective observations or conclusions. The unstructured nature of free text from notes reflecting subjective patient experiences or qualitative insights from healthcare professionals especially adds a further layer of complexity. Furthermore, the contrast between patient and doctor notes reflects the dual perspectives of symptoms and formal diagnoses. In essence, clinical data's unique blend of structured and unstructured components, along with its multidisciplinary nature, necessitates specialized methodologies for comprehensive analysis and interpretation in the realm of healthcare. Further, because clinical data contains sensitive, personal information about patients, there are additional security and ethics concerns in the handling and management of clinical data. + +## Major clinical data types + +Clinical data can come in many different forms, including + +* patient demographics +* medical history or records such as diagnoses, lab results, vital signs, medication records, or procedure history +* genetic reports +* health monitor data +* images +* scanned documents, and notes written by physicians, nurses, and other clinicians +* survey/ case report form (CRF responses) +* and more ... + +Clinical data refers to information collected from patients during healthcare delivery, clinical trials, and medical research and could include demographics, medical history, lab results, imaging, treatment outcomes, and more + +Some sources of clinical data are more prevalent and readily obtainable than others. For instance, notes, demographics, images, and histories or observations/records (lab results, vitals, medications, procedures) are often stored directly in electronic medical record systems making them more easily accessible and so are the focus of most Electronic Health Record (EHR) data research efforts. + +### Structured data + +Observational records such as test results and demographic data are often collectively referred to as “structured data”, as they are stored in electronic health record databases and often provided to researchers in tabular form. Structured data types frequently used in EHR research consist of comprehensive longitudinal records of a patient's interactions with a healthcare system and may include demographics, diagnoses, lab values, procedures, vitals, and medication records. + +Electronic health records include many different kinds of records on individuals over time, including, clinical notes, family history information, lab result, images, and medication information. The image shows data on the same individual over a period of time. + +The structured nature of this data allows for it to be stored in tables which may be indexed by a patient or visit ID and often include timestamps and other supporting descriptors. For example, medication orders might specify the drug name, class, dose, unit, quantity, route, frequency, and other instructions. + +Structured data tables often describe entries in terms of codes from standardized vocabularies. Diagnoses might be described with codes from the International Classification of Diseases (ICD) vocabulary, lab tests with Logical Observation Identifiers Names and Codes (LOINC), medications with National Drug Code (NDC), and procedures with Current Procedural Terminology (CPT) codes. These terms, or "billing codes", provide a common foundation that can be invaluable for identifying patients with a specific disease or who have received specified medications, particularly when integrating data from multiple sources. + +Example of structured data, a table that includes patient id numbers, billing codes, dates, blood pressure measurements, weight measurements, height measurements, and prescribed medications coded using the National Drug Code (NDC) + +As we've described it, structured clinical data is expected to have similarities, although specific details may vary based on the type of EHR being used and any customizations to the EHR for the specific environment or institution in which data was collected (e.g., any specialized pre-processing by institutional research offices prior to providing data to researchers). So it is important for researchers to consider differences present in their data if they've obtained it from multiple contexts or institutions. + +### Unstructured data / clinical notes + +Clinical notes are, perhaps unsurprisingly, generally shared as seemingly straightforward text files. However, the simple format should not be taken as a suggestion that the data are easy to interpret. Some EHR systems contain literally dozens of types of notes, covering specialties such as pathology or surgery; specific moments in care such as admission or discharge; particular procedures such as colonoscopies; patient-provider interactions such as telehealth or phone encounters, and many others. In addition to differing in content, these sources may have different layouts and formats, ranging from free-form reports to structured SOAP (subjective, objective, assessment, and plan) formats or even templated procedure reports. Understanding the types of notes available in a given context and where relevant data might be found is a key step in effectively using clinical notes. + + +Unstructured Data - Data without specific format: includes images, pathology reports, radiology reports, clinical notes, discharge summaries and more. Includes an image of an x-ray and some clinical notes that states: Patient reports a sharp pain in the right lower abdomen for the past 24 hours.Temperature 98.6°F, BP 120/80, tenderness in the right lower quadrant. Suspected appendicitis. Refer to surgery for evaluation, initiate IV fluids, and pain management. + + +When used in EHR research, both structured data and clinical notes are generally de-identified to protect patient privacy. Patient ID numbers might be replaced with new identifiers, with linkages maintained by institutional “honest brokers” [@Dhir2008] charged with providing clinical data for research purposes. In some cases, dates may be changed as well. Clinical notes are generally “de-identified” through specialized software designed to remove names, dates, locations, and other sensitive details. Researchers working with institutions to access clinical data should be sure to understand local data de-identification practices. + + +## How to acquire clinical data + +### Secondary Sources + + +## Description of data + +### File types + +### Metadata + + +## Summary diff --git a/docs/no_toc/01-data_types_files/figure-html/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ_g3385bea4ad0_0_0.png b/docs/no_toc/01-data_types_files/figure-html/1ivDTcLjb2078O0GemkSeCgC1jmxk4fMsiFQaPaer9mQ_g3385bea4ad0_0_0.png new file mode 100644 index 0000000000000000000000000000000000000000..4e606c1c720e542f24ed2c679e80038e82d8a9f5 GIT binary patch literal 52457 zcmeFYbyU@B)HTXRP(m7ML_$&;q+!z_-O}CN(kb1oNH+*bOG^kMAe)w!F6r*?;dtJ2 z&O62(sf2AIp=zIxT3ry8VV5#92^{)w3L`K9NdF1aB%P>Nbul4 z^+Jj-;Nbk>q{W0)-{}3$c=Q2BoVbUwm68-Aj-oY)BKO&|k@Q?7DHTlde54U!&qV51 zspx?td?U>+`5#nE=)ciWggsL`5?DFuY3+O7kUq2WD)Vmdt-jmZ+*B)h`*Xx=fB5%*4nvpzhnQ1gxoY;MfC4@CP>1+C;B_e ziPsF-B{_cp=hKK5|)}|8p?b0Lnk#|B!;f!TEQFO#JgFveOIH4a~pKiQRopku6&M 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Robbertz, Ph.D.^1^; and Meghan E. McGrady, Ph.D.^1,2^ + +**NOTE:** Jennifer Kelleher, Ph.D.^1^ and Abigail S. Robbertz, Ph.D.^1^ contributed equally. + +^1^ Center for Adherence and Self-Management, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA + +^2^ Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA + +The work discussed in this section was also supported by the National Cancer Institute at the National Institutes of Health (R21CA263704, K07CA200668) to MEM. JK and ASR are supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development at the National Institutes of Health (T32HD068223). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. + +::: + +Electronic monitoring devices are digital tools that can be used to track health behaviors over time such as: + +* Sleep +* Physical activity +* Medication adherence +* Calorie intake + +Electronic monitoring devices can also be used to assess physical health indicators including: +* Blood glucose levels +* Blood pressure +* Heart rate and heart rate variability +* Oxygen saturation + +Electronic monitoring devices enable researchers to track day-to-day health behaviors in the patient’s **"real-world" setting**. This allows researchers to explore patterns or changes in a patient’s health behavior and provides a richer understanding of **daily behavior over time**. + + +### Benefits of Monitoring Data + +1) Electronic monitoring devices often include data transmission abilities that enable healthcare providers or researchers to access these data in near **real-time** potentially informing intervention and/or medical decision-making. + +1) Electronic monitoring devices also have the potential to produce **more accurate** estimates of health behaviors than alternative strategies (e.g., self-report) as they are not subject to recall bias and can detect efforts to inflate adherence due to social desirability. + +### Considerations + +:::warning +This section is not exhaustive. Research teams are strongly encouraged to consult with experts with experience and training in collecting and analyzing data from specific devices. + +To ensure the outcome variables are aligned with the research question of interest and [ethical and age/developmental considerations](https://pmc.ncbi.nlm.nih.gov/articles/PMC10798216/) (@psihogios_ethical_2024; @modi_pediatric_2012) have been appropriately accounted for, readers are encouraged to consult with researchers in their field who have integrated these measurement strategies into their work. +::: + +#### Medication Adherence + +There are three major components of medical adherence (the tracking of taking medication): + +- Initiation: Starting a prescribed regimen +- Implementation: The amount of which a patient's medication-taking behavior corresponds with the treatment regimen or protocol +- Discontinuation: Stopping a perscribed regimen + +For more information see: + +- [A new taxonomy for describing and defining adherence to medications (Vrijens et al., 2012)](https://pmc.ncbi.nlm.nih.gov/articles/PMC3403197/) +- [Pediatric self-management: A framework for research, practice, and policy. 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zPlXR?P7T;YxyNe&I5m?+(}fp8Pkc4JgGq!n^3dwwv89ty8l*tM+9aS`k%{?jqVp;J z+gLkOcXjQwZONm+qRc|3J^*thk-f>eE9{=B(gRG+vvSGAjD(3Meu5X~>Dx7n6!RZl`xKC4Qej0SdK{9NXTcfm^N}A^q&$a{} zDQtnIHsXk*alKRtO97j50_>?q)zvm>6Oeo>^L!<8u7?F@)B~>+O3eP5xby(63b2dA zQFTiBso*=hI;F77Ja-Sm0FWm1!C9YZ;KfpD<<(wviQagKv&(^H2d{z;5lU?Aov*d5 zlZhu*{Lr}3exSvO4E#Rgd|_AFP=)v4liG>%TAEcbpc_(mq<$+uvtYhe;IbQ+E~}zq zR~@5?Rf1Zpd#yMTQ%^1U{M@3;gY-f3+>Z)>Bv2>&vvMhHD5IbDHS28cYFq+x>~!Zl z)HAB4Kfvc3-(3*j-U386Ax;}A?i$Ds5hK-gy_u~tc`*V`B6?b75*>StSJ?$<(nYJO z`Lpf4L~gXq8sNoXy`bnpcX-;g?^3GX9MqwfTAAUOZpNWT+myEgzDS0=|I01}&FjQgm8r_PGXM1K9k(ym{O-5p4`qJ*rgxup* z7}oqxBsbK@$3fb19Gr`Y4j}fVOKLAdhdZGB?|<%g*h0H$%P~9A{DU}jmP1-AleRRd z9z59K1XO@YD6j)gKB&GVsg*NM{elfH^i{%Ebje!zRrmqgNV;WiruLj5Yl0a3x)ZP# z*KwZcf`bca9O4r@kO2O(`lqkV_se+>BQa=9g}5;zrhGeTxT!x1&LCN*#rXi;>RZ`w z-DC##B0IwLN7Mj_<#LX$o<>lFo+_9KZys8gUx*sK*A#< sJC5ceBcs4lkX;Av8JX+ + +These data can be accessed not only by health professionals, but also by patients through patient portals. EHR data can also be used to enable data-driven interventions such as provider- and patient-facing clinical decision support (CDS) and population health management (PHM). + +CDS has been defined as tools that "provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care" [@Osheroff_2007]. Examples of widely adopted CDS tools with demonstrated effectiveness for cancer prevention, diagnosis, and care include: + + * provider and patient reminders for cancer screening + * decision aids and shared decision-making tools (e.g., for prostate cancer treatment, lung cancer screening) + * AI-supported diagnostic imaging + * chemotherapy decision support + * at-home symptom care + +While CDS tools generally provide decision support focused on a specific patient at a time, PHM are strategies that target specific patient populations [@Swarthout_Bishop_2017]. PHM efforts generally consist of: + + * population algorithms that are applied over EHR and other data sources to identify individuals who are eligible for a specific healthcare intervention (e.g., colorectal cancer screening, tobacco cessation, HPV vaccination) + * patient engagement strategies (e.g., patient portals, text messaging, chatbots, patient navigators) that provide education and access to those interventions + * analytic tools that assess the effectiveness of the PHM program. + +Several PHM programs have demonstrated to be effective in increasing the uptake of cancer prevention. For example, the colorectal cancer screening program at Kaiser Permanente uses digital (i.e., text messaging, patient portal), mailed, and patient navigation approaches to increase colorectal cancer screening by mailing Fecal Immunohistochemical Test (FIT) kits to patients' homes [@Gupta2020]. Also the Cancer Moonshot BRIDGE trial used the GARDE platform [@Bradshaw2022] (ITCR-funded) to identify candidates for genetic testing of hereditary cancer syndromes based on EHR data; and for patient outreach, pre- and post-test education via automated chatbots [@Kaphingst2024]. + + +Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. + +Clinical Decision Support (CDS) can help patients and clinicians make decisions about an individual’s care, while Population Health Management (PHM) can help identify individuals for interventions and engagement. The image shows a single person getting a colorectal screening reminder for CDS and a population being identified for possibly needing colorectal screening for PHM. + +While some CDS and PHM approaches have been successfully adopted widely, emerging technologies such as the use of generative AI approaches to analyze diagnostic imaging, large language models (LLMs) to extract information from narrative texts (e.g., clinical notes), LLM-based chatbots to communicate with patients, and digital health tools such as home-based sensors are creating unprecedented opportunities for next generation CDS and PHM. These approaches have the potential to enable significant breakthroughs through the implementation of patient-tailored cancer prevention and care at a population scale. Nevertheless, substantial research is needed to ensure effective and fair implementation of these CDS and PHM interventions. + + + +## Types of questions that can be asked with clinical data + +### Risk Prediction + +Risk prediction in clinical research involves using data to assess the likelihood of certain outcomes or events occurring in patients. This could include predicting the risk of developing a particular disease, experiencing a specific complication, or responding to a treatment. + +Data used for risk prediction can come from various sources, including: + + * Clinical Data: This includes patient demographics, medical history, laboratory results, and imaging studies. + * Genetic Data: Genetic information, such as DNA sequencing results, can provide valuable insights into an individual's susceptibility to certain diseases. + * Environmental and Lifestyle Data: Factors such as diet, exercise habits, smoking status, and environmental exposures can influence disease risk and may be included in risk prediction models. + * Biomarkers: Biological markers indicative of disease or physiological processes can be used as predictors in risk models [@Bodaghi_Fattahi_Ramazani_2023]. + +Once relevant data is collected, statistical and machine learning techniques can be applied to develop predictive models. These models aim to identify patterns and relationships within the data that are associated with the outcome of interest. Common techniques include logistic regression, decision trees, random forests, support vector machines, and neural networks. + +After the model is trained on a dataset, it can be validated using independent datasets to assess its performance and generalizability. Once validated, the model can be used to predict risk in new patients based on their individual characteristics and data. + +Clinical prediction rules are a subset of risk prediction models, specific to clinical research. + +Examples of risk prediction models ... + +Risk prediction models are important because ... + +Overall, risk prediction in clinical research allows healthcare professionals to identify individuals at higher risk of certain outcomes, enabling targeted interventions, personalized treatments, and more efficient resource allocation. + +### Cohort identification for research + +Clinical data plays a crucial role in cohort identification for research purposes. Researchers typically use electronic health records (EHRs), medical databases, or registries to identify cohorts based on specific criteria such as age, gender, medical conditions, treatments, medications, and outcomes. Advanced data mining and natural language processing techniques can also be employed to extract relevant information from unstructured data sources like clinical notes. Once cohorts are identified, researchers can analyze the data to study disease progression, treatment effectiveness, and outcomes. + +Cohort identification is important regardless of research study type, but to provide specific examples: + +* Research Design: Identifying cohorts allows researchers to design studies with appropriate inclusion or exclusion criteria. By selecting specific groups of individuals with similar characteristics or exposures, researchers can investigate hypotheses effectively. +* Clinical Insights: Cohort studies enable researchers to observe the natural history of diseases, track outcomes over time, and assess the effectiveness of interventions or treatments. Understanding how different factors influence disease progression or treatment response can inform clinical decision-making and improve patient care. +* Epidemiological Studies: Cohort identification is crucial for epidemiological research to understand the incidence, prevalence, and risk factors associated with diseases. By following cohorts over time, researchers can identify trends, patterns, and associations that contribute to our understanding of disease causation and prevention. +* Precision Medicine: Identifying cohorts based on genetic profiles, biomarkers, or other specific characteristics allows researchers to tailor treatments and interventions to individual patients. This approach, known as precision medicine, aims to optimize therapeutic outcomes while minimizing adverse effects. +* Healthcare Policy and Planning: Cohort studies provide valuable data for informing healthcare policies, resource allocation, and public health strategies. By identifying high-risk populations or groups with specific healthcare needs, policymakers can develop targeted interventions to improve health outcomes and reduce disparities. + +### Case report forms (CRFs) + +In clinical research, case report forms (CRFs) are essential tools for collecting standardized data from study participants. Note that case report forms are any paper or form that will be filled in at the case or participant level. By that definition, even a consent form is a case report form! And each clinical study may utilize multiple CRFs (e.g., one for consent, another for medical history, another for reporting any adverse effects). CRFs are useful for several tasks already discussed within this chapter -- specifically, tracking adverse events or outcomes (like those discussed in the Risk Prediction section) or tracking demographics and medical history for identifying cohorts (as discussed in the Cohort Identification section). CRFs are also useful for topics this chapter will discuss in later sections (e.g., Retrospective analyses). + +Designing CRFs that are accessible and sensitive to the different needs of participants requires careful consideration. Questions within CRFs should be formulated to gather comprehensive and accurate clinical data while ensuring participants feel safe, respected, and comfortable. This section explores the types of questions that can be asked using CRFs with a focus on **accessibility, sensitivity, specificity, and comfort for people** + +::: {.notice} + +While within a study, CRFs help to ensure standardization in data collection, there may be a lack of standardization when comparing data between studies if each study did not use the same CRFs or comparable/subsets of questions within forms. This section will provide guidelines for types of questions that may be found within CRFs and writing these questions; however, there aren't necessarily templates that have been used widely within the field. Further, your specific study needs may also require different or additional types of questions -- the guidelines within this section are not exhaustive/all-encompassing of what may be encountered within the field. + +::: + +Here, we explore the different categories of questions that may be included within case report forms: + +1. **Demographic and Socioeconomic Questions** + +Capturing demographic and socioeconomic data is fundamental in clinical research to understand the background of study participants. However, these questions must be asked in a manner that respects privacy, avoids assumptions, and helps identify people with different life experiences. It’s also good practice in order to respect privacy to include an option allowing the participants to leave the question unanswered or respond with “prefer not to answer”. + +- **Sex and Gender**: When asking about gender and sex include an option of “prefer to self-describe” to capture more information. +- **Ethnicity and Race**: Ethnicity and race questions should be specific and use respectful language, allowing participants to self-identify rather than selecting from a predefined list. +- **Socioeconomic Status**: Questions about employment, income, or education should be framed to capture social determinants of health without making participants feel judged. For example, asking, "What is your current employment status?" with choices that include full-time, part-time, unemployed, student, and unable to work can help gather relevant data without stigma. + +It is essential to use neutral, non-judgmental language and to explain why these questions are being asked, ensuring participants understand the relevance of their responses. + +2. **Health and Medial History Questions** + +Health and medical history questions provide critical information about baseline conditions and potential risk factors of participants. These questions should be framed clearly and respectfully to avoid any discomfort. + +- **Medical Conditions and History**: Questions about past and present health conditions should use clear, accessible language. For example, "Have you ever been diagnosed with any of the following conditions? (Please check all that apply)" followed by a comprehensive list containing all necessary options. +- **Medication Use**: Questions about current and past medications should include over-the-counter and alternative therapies, and space should be provided for free-text responses to capture additional details. +- **Disability and Functional Status**: For many populations, it is important to use person-first language, such as "Do you have any physical, sensory, or cognitive impairments that you would like us to be aware of?" and provide space for participants to describe their specific needs. However, different populations have different preferences. The "Disability Language Style Guide" from the National Center on Disability and Journalism provides some basic guidelines, thorough discussion, and community specific advice on this topic [@ncdj_style_guide]. + +Avoiding medical jargon and providing definitions or examples can help ensure that participants understand the questions, and confidentiality should be emphasized to encourage honest responses. + +3. **Experience and Quality of Life Questions** + +Understanding how health conditions and treatments affect participants' daily lives and well-being is essential, particularly for those from communities who experience health disparities and may also experience unique challenges. + +- **Daily Living and Social Functioning**: Questions like "How often do your health conditions affect your ability to perform daily tasks (e.g., cooking, cleaning, working)?" can help assess the impact on daily life, with options ranging from "never" to "always." +- **Emotional and Psychological Well-Being**: Including questions such as "In the past week, how often have you felt anxious or depressed?" using a scale from "not at all" to "very often" can provide insights into mental health needs. +- **Support Systems and Social Networks**: Asking about social support (e.g., "Do you have someone you can rely on for emotional support?") can help identify participants' needs for social and emotional resources. + +Using sensitive language and providing mental health support resources where needed is crucial when discussing emotional well-being to avoid triggering emotional distress. + +4. **Sexual and Reproductive Health Questions** + +Questions about sexual and reproductive health must be asked with sensitivity, as they can be deeply personal, particularly for groups who may face stigma. + +- **Sexual Orientation and Gender Identity (SOGI)**: Instead of just predefined categories, open-ended questions like "How would you describe your sexual orientation?" and "What is your gender identity?" allow participants to self-identify. Offering the option to skip these questions respects participants' privacy. Options can be provided with a space or text box for the person to fill in their own descriptor, but having the categorical data will allow for easier analysis for those that select one rather than every response being from variable open text responses. +- **Reproductive Health**: Questions about menstrual health, contraception, or pregnancy should be framed neutrally. For example, "Are you currently using any form of contraception? If yes, please specify." +- **Sexual Activity and History**: Questions should be direct but framed sensitively, such as "Are there any sexual health concerns you would like to discuss? Your answers will help us understand how to better support your care needs." + +These questions should always be optional, with confidentiality emphasized to encourage honest, comfortable participation. + +5. **Treatment Preferences and Decision-Making Questions** + +Understanding participants’ preferences for treatment and decision-making is vital for providing patient-centered care, especially for certain groups of people. + +- **Decision-Making Preferences**: Questions like "How involved would you like to be in decisions about your healthcare?" offer a range of choices from “I prefer to make decisions myself” to “I prefer my healthcare provider to make decisions,” allowing participants to express their autonomy. +- **Cultural and Religious Considerations**: Asking, "Are there any cultural, religious, or personal beliefs that we should consider when discussing treatment options with you?" ensures that care is respectful and culturally appropriate. +- **Treatment Burden**: Questions such as "What level of inconvenience or side effects would be acceptable to you when considering a treatment?" help to gauge participants' preferences and comfort levels. + +These questions should be framed to respect participants’ autonomy and encourage honest responses without fear of judgment. + +6. **Accessibility and Accommodation Needs Questions** + +To ensure that all participants can fully engage with the study, it is essential to ask about accessibility and accommodation needs. + +- **Language and Communication Needs**: "What is your preferred language for communication? Do you need an interpreter or translated materials?" These questions help ensure that participants can understand the materials. +- **Physical Accessibility**: Asking, "Do you require any specific accommodations to participate in this study (e.g., wheelchair access, hearing aids, visual aids)?" ensures physical accessibility. +- **Format Preferences**: "Would you prefer to complete this form online, on paper, or verbally with assistance?" helps accommodate different needs and preferences. +Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. + +Providing multiple options and allowing participants to request changes at any time is crucial to accommodate evolving needs. + +7. **Cultural Sensitivity and Identity Questions** + +CRFs should respect different cultural backgrounds, values, and identities without perpetuating biases or assumptions. + +- **Cultural Identity and Practices**: An open-ended question such as "Are there any cultural practices or beliefs that are important for us to be aware of in your care?" allows participants to share relevant information. +- **Dietary Restrictions and Preferences**: Asking, "Do you have any dietary restrictions or preferences that are culturally or religiously motivated?" ensures that these are respected. +- **Community and Belonging**: "Is there anything about your community or background that you would like us to know to provide better care?" encourages participants to share relevant aspects of their identity. + +These questions should be open-ended, allowing participants to skip questions they find irrelevant or uncomfortable. + +8. **Ensuring Comfort, Trust, and Privacy Questions** + +Fostering a sense of safety and trust is especially important for individuals who may have experienced discrimination in healthcare settings. + +- **Comfort and Confidentiality**: Asking, "Do you feel comfortable with the way your information is being collected and stored? Are there any specific concerns you would like to address?" helps build trust. +- **Feedback and Preferences**: "Is there anything about this form or the study process that you find confusing, uncomfortable, or concerning?" invites participants to share their feedback. +- **Consent and Voluntary Participation**: Questions like, "Would you like to be contacted about the results of this study or for future research opportunities? Participation is entirely voluntary." reinforce autonomy and respect. + +Reminding participants of the confidentiality and voluntary nature of their involvement can help foster a trusting environment. + +::: {.warning} + +The guidelines above sometimes suggest use of open-ended questions where participants would provide free-text responses rather than selecting pre-defined categories. This will require researchers to process those free-text responses and may decrease the overall standardization. + +::: + +Designing specific and sensitive CRFs for clinical studies requires a thoughtful approach that respects the different backgrounds, privacy, and comfort of all participants, especially those from communities that experience health disparities. By using accessible language, offering multiple options, respecting autonomy, and providing a safe space for participants to express themselves, researchers can gather meaningful and accurate data while ensuring participants feel valued and respected. These considerations are vital to fostering clinical research that captures enough information about a wide variety of individuals. Case report forms (CRFs) are often tailored to specific studies and may vary widely in structure and content, lacking standardization across different projects. Despite this, certain themes are commonly expected in CRFs, including sections on participant demographics, medical history, treatment outcomes, and adverse events, ensuring essential data collection across a variety of study designs. By thoughtfully addressing both unique study requirements and universally relevant data points, researchers can optimize CRFs for consistency across clinical studies. + + +### Clinical studies and trials + +Clinical data generated from clinical trials and observational studies form the backbone of evidence-based medicine. The ability to analyze and interpret this data enables researchers and clinicians to answer a variety of important questions that directly impact patient care, treatment decisions, and healthcare policies. + +Clinical trials are tightly regulated studies, controlling patient recruitment and monitoring administration and impact of treatments or interventions [@What_are_clinical_trials_2023]. The goals of clinical trials include assessing the safety and efficacy of new treatments or interventions, comparing different/existing treatment options, determining the optimal dosage and administration of interventions, and identifying potential side effects or adverse events. + +::: {.definition} +The NIH's definition of a clinical trial is "a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes." +::: + +The NIH provides 4 questions and additional clarifications to consider when identifying if a study is a clinical trial or just a clinical study [@CTDefinition]. + +::: {.definition} +**Prospectively assigned**: Refers to a pre-defined process (e.g., randomization) specified in an approved protocol that stipulates the assignment of research subjects (individually or in clusters) to one or more arms (e.g., intervention, placebo, or other control) of a clinical trial [@NIHClinicalTrialsProspectively]. +::: + +Here, we explore the different categories of questions that can be addressed using clinical trials data, ranging from the evaluation of treatment efficacy and safety to the exploration of predictive factors for disease prognosis and the personalization of medical care. + +1. **Efficacy and Effectiveness Questions** + +One of the primary objectives of clinical trials is to determine the **efficacy of new treatments or interventions**. Clinical data is used to answer fundamental questions such as "Is the new treatment effective?" and "How does its effectiveness compare to existing treatments?" + +Researchers design studies to measure the **magnitude of treatment effects**, comparing outcomes between treatment and control groups (e.g., a new drug versus a placebo or standard care). + +Beyond controlled settings, data from real-world evidence (RWE) studies can further address questions about the effectiveness of treatments when applied in routine clinical practice. For example, "Is Drug A more effective than Drug B in managing hypertension in a real-world patient population?" Such questions help to assess how treatments perform outside the controlled environment of clinical trials where patient adherence comorbidities, and polypharmacy may influence outcomes. While clinical trials are an important source of real-world data (RWD), they are typically conducted under tightly controlled conditions, which may not fully reflect the complexities of real-world studies. This makes RWE particularly valuable for understanding how treatments work when patients have varying levels of adherence or multiple chronic conditions that are often excluded from clinical trails. + +::: {.definition} +**Comorbidities**: refer to the presence of one or more additional medical conditions or diseases that coexist with a primary condition in a patient. These conditions can occur independently or be related to the primary disease. +::: + +2. **Safety and Tolerability Questions** + +Safety is a critical aspect of any clinical study, and clinical data is essential in identifying and characterizing the safety profile of new treatments. Questions like "What are the common adverse effects of the new treatment?" or "What is the incidence of serious adverse events (SAEs) in patients receiving Drug A?" are addressed by carefully monitoring and recording adverse events throughout the study. Comparative safety questions also arise, such as "Does Drug A have fewer adverse effects compared to Drug B?" These questions are crucial in weighing the benefits of a treatment against its risk and in identifying specific populations that may be at higher risk of adverse reactions. + +::: {.definition} +**Serious Adverse Events (SAEs)**: Any medical occurrence during a clinical trial or medical treatment that results in significant negative outcomes, such as death, life-threatening situations, hospitalization (or its prolongation), persistent or significant disability, or congenital anomalies. SAEs also include any event that requires intervention to prevent one of these outcomes. +::: + +::: {.definition} +**Incidence**: Refers to the rate or number of new cases of SAEs that occur within a specific population during a defined time period, typically during a clinical trial or study. Is it usually expressed as a proportion, such as the number of SAEs per 100 or 1,000 participants, and helps measure how frequently these serious events happen among those exposed to a particular treatment or intervention. +::: + +3. **Comparative Effectiveness and Cost-Effectiveness Questions** + +Beyond safety and efficacy, clinical data can be used to evaluate the comparative effectiveness of different treatments and their cost-effectiveness. These questions often guide healthcare policy and clinical guidelines: "Is Drug A more cost-effective than standard care for managing chronic heart failure?" and "What is the cost per quality-adjusted life year (QALY) gained with the new intervention?" Such analyses provide valuable information for stakeholders, including healthcare providers, payers, and policymakers, to make informed decisions on resource allocation. + +::: {.definition} +**Cost per quality-adjusted life year (QALY)** is a metric used in health economics to assess the value of a medical intervention by measuring the cost of gaining one year of life adjusted for its quality. A QALY incorporates both the **quantity** and **quality** of life lived, where one QALY is equivalent to one year of life in perfect health. If a treatment improves both the length and quality of life, it earns more QALYs. The **cost per QALY** is calculated by dividing the cost of the treatment by the number of QALYs gained, helping to determine the cost-effectiveness of healthcare interventions. Lower cost per QALY indicates better value for money in terms of health benefits achieved [-@Drummond_Sculpher_Claxton_Stoddart_Torrance_2015]. +::: + +4. **Mechanistic and Biomarker Questions** + +Clinical studies often explore not just whether a treatment works, but also how it works. Mechanistic questions delve into the underlying biological pathways affected by a treatment. For instance, "Does Drug A work by inhibiting a specific enzyme involved in the disease process?" Biomarker data can play a key role in such analyses, helping to identify molecular markers that predict response to treatment. Questions like "Are there biomarkers that can predict which patients are more likely to benefit from Drug A?" help in developing targeted therapies and personalized treatment approaches. + +5. **Predictive and Prognostic Questions** + +Clinical trials and studies frequently aim to identify factors that predict disease outcomes or treatment responses. For example, "What baseline characteristics predict a better response to Drug A?" or "What are the predictors of mortality in patients with severe heart failure?" Such prognostic and predictive questions are crucial for identifying high-risk patients, guiding treatment decisions, and developing clinical guidelines. Identifying patient subgroups that benefit more or less from a treatment also facilitates personalized medicine approaches, ensuring that the right treatment is delivered to the right patient at the right time. + +With the advent of personalized medicine, clinical data is increasingly being used to answer questions tailored to individual patient profiles. For instance, "What patient characteristics modify the effect of the treatment?" or "Can we predict which patients are most likely to benefit from Drug A using machine learning models?" These questions are at the forefront of precision medicine, where the goal is to customize healthcare to each patient based on their unique genetic, biomarker, and clinical characteristics. + +6. **Quality of Life and Patient-Reported Outcome Questions** + +In modern clinical research, the impact of treatments on patients' quality of life and their subjective experiences has gained prominence. Questions such as "How does Drug A affect patients' quality of life compared to standard care?" or "What are the patient-reported outcomes associated with the new intervention?" are increasingly addressed in clinical trials. Data from quality of life assessments which are standardized and validated questionnaires (for example, the Health Assessment Questionnaire or HAQ), patient-reported outcome measures (PROMs, tools or questionnaires used to assess a patient's health status or health-related quality of life directly from the patient's perspective), and other patient-centric endpoints (Smoking History, Surgical History, etc.) provide valuable insights into how treatments affect patients beyond traditional clinical endpoints. + +7. **Longitudinal and Follow-Up Questions** + +Long-term follow-up studies are essential for understanding the durability of treatment effects and any delayed adverse effects. Questions like "What are the long-term outcomes associated with Drug A?" and "Is there a sustained benefit of Drug A in reducing symptoms after five years?" are vital for assessing the overall value of treatments. Such longitudinal analyses help in determining the optimal duration of therapy, the need for maintenance treatments, and the overall benefit-risk profile of an intervention. + +8. **Safety in Special Populations Questions** + +Clinical data can also be used to evaluate the safety and efficacy of treatments in special populations, such as children, pregnant women, or elderly patients. Questions such as "Is Drug A safe for use in pregnant women?" and "What is the risk of adverse events when Drug A is administered to patients with multiple comorbidities?" are essential for developing age- and condition-specific clinical guidelines. These questions help ensure that treatments are tailored to the needs of different patient populations, minimizing harm and maximizing benefits. + +9. **Adherence and Compliance Questions** + +Patient adherence to prescribed treatment regimens can significantly impact the outcomes of clinical studies. Questions like "What factors influence patient adherence to the new treatment?" and "How does adherence affect treatment efficacy?" are addressed through analyzing adherence data through monitors if a regulated study, or with interim/baseline analysis if non-regulated. Understanding these factors can lead to interventions that improve adherence and, consequently, the effectiveness of the treatment. + +10. **Real-World Evidence and Generalizability Questions** + +Real-world evidence (RWE) studies help bridge the gap between clinical trial results and clinical practice. Questions such as "How generalizable are the results of this clinical trial to the broader population?" or "What is the impact of real-world use patterns on treatment outcomes?" are essential for understanding how well clinical trial findings apply to everyday clinical settings. These questions help determine if the benefits observed in clinical trials can be replicated in diverse patient populations under routine care conditions. + +11. **Prevention and Risk Reduction Questions** + +Clinical data is also crucial in answering questions related to prevention and risk reduction strategies. For example, "Can the new treatment reduce the risk of developing diabetes in high-risk individuals?" or "What factors are associated with a reduced risk of cardiovascular disease in a large cohort study?" These questions are fundamental in preventive medicine, guiding public health interventions and informing clinical practice. + + +Clinical Data provides a wealth of information that can be leveraged to answer a broad array of questions in clinical trials and studies. From understanding treatment efficacy and safety to exploring long-term outcomes, quality of life impacts, and the potential for personalized medicine, clinical data is foundational to advancing medical knowledge and improving patient care. Case report forms (CRFs) are instrumental in identifying patient cohorts or subgroups, documenting baseline characteristics, and capturing information on comorbidities, while also monitoring adherence data, which are essential for ensuring accurate and meaningful analysis. The type of questions that can be asked and answered are continually evolving as new data sources, analytical methods, and research paradigms emerge, further enriching the field of clinical research. + + +### Retrospective analysis + +Retrospective analysis involves the examination of pre-existing clinical data to answer specific research questions, explore hypotheses, and identify patterns or trends. This method leverages historical data collected from medical records, administrative databases, registries, electronic health records (EHRs), or other sources of clinical information. The types of questions that can be asked through retrospective analysis span a wide range of clinical and epidemiological domains. The questions typically focus on understanding patient characteristics, disease epidemiology, treatment outcomes, risk factors, healthcare utilization, and more. + +Here, we explore the different categories of questions that can be addressed using retrospective data analysis: + +1. **Descriptive Questions** + +Descriptive questions aim to summarize and describe the characteristics of a patient population, disease, or healthcare process. They provide a foundational understanding of a dataset and are often the starting point for more complex analyses. + +- **Patient Demographics and Characteristics**: What are the demographic profiles (age, gender, ethnicity, socioeconomic status) of patients diagnosed with a specific condition? What are the common comorbidities and risk factors in this population? +- **Disease Prevalence and Incidence**: What is the prevalence or incidence of a specific disease or condition in a particular population or geographic area over a defined period? +- **Clinical Presentation**: What are the most common presenting symptoms, clinical signs, or laboratory findings associated with a specific disease or condition? + +These questions help in understanding the baseline characteristics and epidemiology of diseases, which is crucial for planning further studies or developing public health interventions. + +2. **Treatment and Intervention Questions** + +Retrospective data can be used to examine the real-world effectiveness, safety, and patterns of treatment and interventions. These questions are critical for understanding how treatments are applied in clinical practice and their outcomes: + +- **Treatment Effectiveness**: What is the effectiveness of a specific treatment or intervention in reducing symptoms, improving quality of life, or achieving clinical outcomes in a real-world setting? +- **Adherence and Persistence**: What are the rates of adherence and persistence with a prescribed treatment regimen over time? What factors are associated with higher or lower adherence rates? +- **Comparative Effectiveness**: How does the effectiveness of one treatment compare to another in a similar population? What are the relative benefits and risks associated with different treatment options? + +By examining historical data, researchers can gain insights into how different treatments perform outside the controlled environment of clinical trials, thereby informing clinical decision-making and guidelines. + +3. **Outcome and Prognostic Questions** + +Understanding patient outcomes and prognostic factors is central to retrospective analyses. These questions focus on the end results of healthcare practices and patient management, including survival, complications, and quality of life: + +- **Survival and Mortality**: What are the survival rates and mortality rates associated with a particular disease, condition, or treatment? What factors are associated with increased or decreased survival rates? +- **Complication Rates**: What are the rates and types of complications associated with specific diseases, surgeries, or treatments? Are there identifiable risk factors for these complications? +- **Prognostic Factors**: What are the key prognostic factors (e.g., age, stage of disease, comorbidities) that influence the outcomes of patients with a particular condition? + +These questions help identify factors that influence patient outcomes, guiding clinicians in predicting disease progression and tailoring individual treatment plans. + +4. Risk Factor and Predictive Modeling Questions + +Retrospective analyses are often used to identify potential risk factors for diseases and develop predictive models. These questions aim to uncover associations between variables and outcomes to help predict future events: + +- **Risk Factors for Disease**: What are the risk factors associated with developing a particular disease or condition? Are there demographic, genetic, behavioral, or environmental factors that significantly increase the risk? +- **Prediction Models**: Can we develop a predictive model to estimate the likelihood of disease progression, relapse, or adverse outcomes based on historical patient data? +- **Multivariable Analysis**: How do multiple factors interact to influence the risk of an outcome? Are there synergistic or antagonistic effects between different risk factors? + +These questions are critical for developing tools that help clinicians assess risk and make more informed decisions in preventive care and early intervention. + +5. **Healthcare Utilization and Cost Questions** + +Understanding how healthcare resources are utilized and the associated costs is important for optimizing healthcare delivery. Retrospective data can provide valuable insights into patterns of care and resource allocation for questions such as: + +- **Healthcare Resource Utilization**: What are the patterns of healthcare utilization (e.g., hospitalizations, emergency room visits, outpatient visits) for patients with a specific condition? How does utilization vary by patient demographics, disease severity, or geographic location? +- **Cost of Care**: What are the direct and indirect costs associated with managing a specific disease or condition? How do different treatment options impact overall healthcare costs? +- **Cost-Effectiveness**: How does the cost-effectiveness of one treatment or intervention compare to another? What are the economic impacts of different clinical management strategies? + +These questions can inform healthcare policy, budget planning, and the development of strategies to improve cost-effectiveness and access to care. + +6. **Quality of Care and Patient Safety Questions** + +Retrospective analysis can be used to evaluate the quality and safety of care provided to patients, identifying areas for improvement and potential patient safety concerns, helping to answer questions such as: + +- **Quality Indicators**: Quality indicators are standardized measures of health care quality and may focus on more specialized groups of care like preventative, inpatient or emergency care, pediatric or maternal health, etc. What are the levels of observed mortality or morbidity for a certain population in a specific setting? How utilized are certain procedures? [@AHRQ] +- **Adverse Events and Safety**: What is the incidence of adverse events, medication errors, or healthcare-associated infections in a particular patient population? What factors are associated with higher or lower rates of adverse events? +- **Patient Satisfaction and Experience**: What are the reported levels of patient satisfaction with healthcare services? How does satisfaction vary by demographic or clinical characteristics? + +These questions help in assessing healthcare quality, identifying gaps in care, and developing interventions to enhance patient safety and satisfaction. + +7. **Temporal and Trend Analysis Questions** + +Retrospective studies often involve analyzing temporal patterns to understand how diseases, treatments, or outcomes change over time. These questions help in recognizing trends and patterns that can inform future research and policy decisions: + +- **Temporal Trends in Disease Incidence and Prevalence**: How have the incidence and prevalence of a specific disease changed over the past decade? Are there seasonal or cyclical patterns in disease occurrence? +- **Trends in Treatment Practices**: How have treatment practices and prescribing patterns evolved over time? Are there emerging trends in the use of certain medications or interventions? +- **Trends in Outcomes and Survival**: How have patient outcomes, such as survival rates or complication rates, changed over time with advances in treatment and healthcare delivery? + +These questions are essential for monitoring public health trends, evaluating the impact of interventions, and planning future healthcare strategies. + +8. **Subgroup and Stratified Analysis Questions** + +Subgroup analyses can help uncover differences in disease characteristics, treatment responses, and outcomes among different patient groups. This type of analysis is particularly valuable for personalized medicine and addressing questions related to health disparities: + +- **Differences by Demographic Group**: How do treatment outcomes, risk factors, or disease characteristics differ between various demographic groups (e.g., age, gender, race/ethnicity)? +- **Subgroup-Specific Effectiveness**: Does a particular treatment work better in one subgroup of patients compared to another? What factors might explain these differences? +- **Health Disparities**: Are there disparities in healthcare access, treatment outcomes, or disease burden across different socioeconomic or minority groups? + +Questions focusing on subgroups are crucial for understanding variations in care and outcomes, identifying at-risk populations, and developing targeted interventions. + + +Retrospective analysis of clinical data allows researchers to ask a wide range of questions that provide valuable insights into patient characteristics, disease epidemiology, treatment effectiveness, outcomes, risk factors, healthcare utilization, and more. By carefully formulating questions and analyzing historical data, researchers can uncover patterns, identify trends, and generate evidence that informs clinical practice, healthcare policy, and future research directions. This chapter outlines the various categories of questions that can be addressed through retrospective analysis, highlighting the potential of this approach to advance knowledge and improve patient care in diverse clinical settings. + + +## Research Design Considerations + +Researchers need to intentionally use methods earlier in the research process than data analysis to manage data biases associated with clinical data, especially EHR. + +One of the most important challenges in using EHR data in cancer research is that, as in many other fields, healthcare data are plagued with several types of biases that result from disparities in the delivery of healthcare. Overall, cancer research has historically relied on data from high resource academic medical centers, which disproportionately provide care to patients who are White, have high socioeconomic status, and live in urban areas. As a result, medical knowledge produced from these data have disproportionately benefited those patients. Different sources of bias are prevalent in EHR data. For example, + +* *Information representativeness bias* occurs when certain groups are disproportionately less present in the EHR because they have no contact with the healthcare system. +* *Information presence bias*, on the other hand, occurs when certain groups may be represented in the EHR, but have disproportionately less comprehensive healthcare data due to issues such as overall lower access to and use of healthcare service, lack of a primary care provider, lack of access to specialty care, and lack of access to digital resources (e.g., patient portals, home sensors, telehealth) that can be used to provide healthcare data. +* *Treatment biases* happen when certain groups receive disproportionate access to more advanced treatments, which is often determined by social drivers such as insurance, distance, health literacy, and socioeconomic status. +* *Algorithm bias* further amplifies these previous sources of bias by leveraging biased EHR data to make predictions about diagnosis, treatment and prognosis that are used by clinicians to make potentially biased healthcare decisions, which are then documented in the EHR. + +Recent advances in sophisticated and costly technology such as genetic testing, artificial intelligence, and digital health, which are disproportionately available in high resource healthcare systems further compound the problem. Therefore, cancer researchers increasingly need to use intentional methods to prevent, identify, and correct for biases in EHR data. For example, the National Institutes of Health Pragmatic Trials Collaboratory has made several recommendations to address EHR data biases in research [@BoydCCT2023; @BoydJAMIA2023]: + +* Include data from low resource healthcare settings such as community health centers that provide care for patients who have low socioeconomic status and live in rural areas. +* Engage with communities during study design and study conduct to ensure proper data collection, analysis, and representation. +* Use data collection methods for self-reported data that rely on more accessible technology such as text messaging, using accessible and culturally adapted communication. +* Include subgroup analysis by different demographic groups according to variables such as socioeconomic status, race, ethnicity, sex, geographical location, and social determinants of health. + +With such approaches, cancer researchers while aiming to avoid exacerbating health disparities, in addition help to reduce disparities. + 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+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
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+ + + + + + + + + + + + + diff --git a/docs/no_toc/About.md b/docs/no_toc/About.md new file mode 100644 index 0000000..6d2c2c9 --- /dev/null +++ b/docs/no_toc/About.md @@ -0,0 +1,143 @@ +--- +title: "Clinical Data Analysis for Cancer Informatics" +--- + +# About the Authors {-} + +These credits are based on our [course contributors table guidelines](https://github.com/jhudsl/DaSL_Course_Template_Bookdown/wiki/How-to-give-credits). + +  +  + +|Credits|Names| +|-------|-----| +|**Pedagogy**|| +|Lead Content Instructor(s)|[FirstName LastName](link to personal website)| +|Lecturer(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involved| Delivered the course in some way - video or audio| +|Content Author(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involved | If any other authors besides lead instructor| +|Content Contributor(s) (include section name/link in parentheses) - make new line if more than one section involved| Jennifer Kelleher, Ph.D.1*; Abigail S. Robbertz, Ph.D.| +|Content Editor(s)/Reviewer(s) | Julia K. Herriott, B.S.| +|Content Director(s) | Helped guide the content direction| +|Content Consultants (include chapter name/link in parentheses or word "General") - make new line if more than one chapter involved | Gave high level advice on content| +|Acknowledgments| Gave small assistance to content but not to the level of consulting | +|**Production**|| +|Content Publisher(s)| Helped with publishing platform| +|Content Publishing Reviewer(s)| Reviewed overall content and aesthetics on publishing platform| +|**Technical**|| +|Course Publishing Engineer(s)| Helped with the code for the technical aspects related to the specific course generation| +|Template Publishing Engineers|[Candace Savonen](https://www.cansavvy.com/), [Carrie Wright](https://carriewright11.github.io/)| +|Publishing Maintenance Engineer|[Candace Savonen](https://www.cansavvy.com/)| +|Technical Publishing Stylists|[Carrie Wright](https://carriewright11.github.io/), [Candace Savonen](https://www.cansavvy.com/)| +|Package Developers ([Leanbuild](https://github.com/jhudsl/leanbuild))|[John Muschelli](https://johnmuschelli.com/), [Candace Savonen](https://www.cansavvy.com/), [Carrie Wright](https://carriewright11.github.io/)| +|**Art and Design**|| +|Illustrator(s)| Created graphics for the course| +|Figure Artist(s)| Created figures/plots for course| +|Videographer(s)| Filmed videos| +|Videography Editor(s)| Edited film| +|Audiographer(s)| Recorded audio| +|Audiography Editor(s)| Edited audio recordings| +|**Funding**|| +|Funder(s)| Institution/individual who funded course including grant number| +|Funding Staff| Staff members who help with funding| + +  + + +``` +## ─ Session info ─────────────────────────────────────────────────────────────── +## setting value +## version R version 4.3.2 (2023-10-31) +## os Ubuntu 22.04.4 LTS +## system x86_64, linux-gnu +## ui X11 +## language (EN) +## collate en_US.UTF-8 +## ctype en_US.UTF-8 +## tz Etc/UTC +## date 2026-05-10 +## pandoc 3.1.1 @ /usr/local/bin/ (via rmarkdown) +## +## ─ Packages ─────────────────────────────────────────────────────────────────── +## package * version date (UTC) lib source +## bookdown 0.46 2025-12-05 [1] CRAN (R 4.3.2) +## bslib 0.6.1 2023-11-28 [1] RSPM (R 4.3.0) +## cachem 1.0.8 2023-05-01 [1] RSPM (R 4.3.0) +## chromote 0.5.1 2025-04-24 [1] CRAN (R 4.3.2) +## cli 3.6.5 2025-04-23 [1] CRAN (R 4.3.2) +## devtools 2.4.5 2022-10-11 [1] RSPM (R 4.3.0) +## digest 0.6.34 2024-01-11 [1] RSPM (R 4.3.0) +## dplyr 1.1.4 2023-11-17 [1] RSPM (R 4.3.0) +## ellipsis 0.3.2 2021-04-29 [1] RSPM (R 4.3.0) +## evaluate 1.0.5 2025-08-27 [1] CRAN (R 4.3.2) +## fansi 1.0.6 2023-12-08 [1] RSPM (R 4.3.0) +## fastmap 1.1.1 2023-02-24 [1] RSPM (R 4.3.0) +## fs 1.6.3 2023-07-20 [1] RSPM (R 4.3.0) +## generics 0.1.3 2022-07-05 [1] RSPM (R 4.3.0) +## gitcreds 0.1.2 2022-09-08 [1] RSPM (R 4.3.0) +## glue 1.7.0 2024-01-09 [1] RSPM (R 4.3.0) +## hms 1.1.3 2023-03-21 [1] RSPM (R 4.3.0) +## htmltools 0.5.7 2023-11-03 [1] RSPM (R 4.3.0) +## htmlwidgets 1.6.4 2023-12-06 [1] RSPM (R 4.3.0) +## httpuv 1.6.14 2024-01-26 [1] RSPM (R 4.3.0) +## httr 1.4.7 2023-08-15 [1] RSPM (R 4.3.0) +## jquerylib 0.1.4 2021-04-26 [1] RSPM (R 4.3.0) +## jsonlite 2.0.0 2025-03-27 [1] CRAN (R 4.3.2) +## knitr 1.50 2025-03-16 [1] CRAN (R 4.3.2) +## later 1.3.2 2023-12-06 [1] RSPM (R 4.3.0) +## lifecycle 1.0.4 2023-11-07 [1] RSPM (R 4.3.0) +## magrittr 2.0.3 2022-03-30 [1] RSPM (R 4.3.0) +## memoise 2.0.1 2021-11-26 [1] RSPM (R 4.3.0) +## mime 0.12 2021-09-28 [1] RSPM (R 4.3.0) +## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.3.0) +## ottrpal 2.0.0 2025-12-16 [1] Github (ottrproject/ottrpal@6d1267e) +## pillar 1.9.0 2023-03-22 [1] RSPM (R 4.3.0) +## pkgbuild 1.4.3 2023-12-10 [1] RSPM (R 4.3.0) +## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.3.0) +## pkgload 1.4.1 2025-09-23 [1] CRAN (R 4.3.2) +## processx 3.8.6 2025-02-21 [1] CRAN (R 4.3.2) +## profvis 0.3.8 2023-05-02 [1] RSPM (R 4.3.0) +## promises 1.2.1 2023-08-10 [1] RSPM (R 4.3.0) +## ps 1.9.1 2025-04-12 [1] CRAN (R 4.3.2) +## purrr 1.0.2 2023-08-10 [1] RSPM (R 4.3.0) +## R6 2.6.1 2025-02-15 [1] CRAN (R 4.3.2) +## Rcpp 1.0.12 2024-01-09 [1] RSPM (R 4.3.0) +## readr 2.1.5 2024-01-10 [1] RSPM (R 4.3.0) +## remotes 2.4.2.1 2023-07-18 [1] RSPM (R 4.3.0) +## rlang 1.1.6 2025-04-11 [1] CRAN (R 4.3.2) +## rmarkdown 2.25 2023-09-18 [1] RSPM (R 4.3.0) +## rprojroot 2.1.1 2025-08-26 [1] CRAN (R 4.3.2) +## rvest 1.0.5 2025-08-29 [1] CRAN (R 4.3.2) +## sass 0.4.8 2023-12-06 [1] RSPM (R 4.3.0) +## sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.3.0) +## shiny 1.8.0 2023-11-17 [1] RSPM (R 4.3.0) +## spelling 2.3.2 2025-08-18 [1] CRAN (R 4.3.2) +## stringi 1.8.3 2023-12-11 [1] RSPM (R 4.3.0) +## stringr 1.5.1 2023-11-14 [1] RSPM (R 4.3.0) +## tibble 3.3.0 2025-06-08 [1] CRAN (R 4.3.2) +## tidyr 1.3.1 2024-01-24 [1] RSPM (R 4.3.0) +## tidyselect 1.2.0 2022-10-10 [1] RSPM (R 4.3.0) +## tzdb 0.4.0 2023-05-12 [1] RSPM (R 4.3.0) +## urlchecker 1.0.1 2021-11-30 [1] RSPM (R 4.3.0) +## usethis 2.2.3 2024-02-19 [1] RSPM (R 4.3.0) +## utf8 1.2.4 2023-10-22 [1] RSPM (R 4.3.0) +## vctrs 0.6.5 2023-12-01 [1] RSPM (R 4.3.0) +## webshot2 0.1.2 2025-04-23 [1] CRAN (R 4.3.2) +## websocket 1.4.4 2025-04-10 [1] CRAN (R 4.3.2) +## xfun 0.55 2025-12-16 [1] CRAN (R 4.3.2) +## xml2 1.5.1 2025-12-01 [1] CRAN (R 4.3.2) +## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.3.0) +## yaml 2.3.12 2025-12-10 [1] CRAN (R 4.3.2) +## +## [1] /usr/local/lib/R/site-library +## [2] /usr/local/lib/R/library +## +## ────────────────────────────────────────────────────────────────────────────── +``` + + diff --git a/docs/no_toc/References.md b/docs/no_toc/References.md new file mode 100644 index 0000000..48143a7 --- /dev/null +++ b/docs/no_toc/References.md @@ -0,0 +1,3 @@ + +# References {-} + diff --git a/docs/no_toc/_updated_output.yml b/docs/no_toc/_updated_output.yml new file mode 100644 index 0000000..e1e21cc --- /dev/null +++ b/docs/no_toc/_updated_output.yml @@ -0,0 +1,18 @@ +bookdown::gitbook: + css: assets/style_ITN.css + includes: + before_body: assets/big-image-itcr.html + after_body: assets/footer.html + in_header: /__w/Clinical_Data_Management/Clinical_Data_Management/docs/no_toc/assets/links.html + highlight: tango + config: + toc: + collapse: section + before: | + + after: |- +

This content was published with bookdown by:

+

The Johns Hopkins Data Science Lab

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Style adapted from: rstudio4edu-book (CC-BY 2.0)

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Click here to provide feedback

diff --git a/docs/no_toc/about-the-authors.html b/docs/no_toc/about-the-authors.html new file mode 100644 index 0000000..8a9f56c --- /dev/null +++ b/docs/no_toc/about-the-authors.html @@ -0,0 +1,513 @@ + + + + + + + About the Authors | Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

About the Authors

+

These credits are based on our course contributors table guidelines.

+

  + 

+ ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CreditsNames
Pedagogy
Lead Content Instructor(s)FirstName LastName
Lecturer(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involvedDelivered the course in some way - video or audio
Content Author(s) (include chapter name/link in parentheses if only for specific chapters) - make new line if more than one chapter involvedIf any other authors besides lead instructor
Content Contributor(s) (include section name/link in parentheses) - make new line if more than one section involvedJennifer Kelleher, Ph.D.1*; Abigail S. Robbertz, Ph.D.
Content Editor(s)/Reviewer(s)Julia K. Herriott, B.S.
Content Director(s)Helped guide the content direction
Content Consultants (include chapter name/link in parentheses or word “General”) - make new line if more than one chapter involvedGave high level advice on content
AcknowledgmentsGave small assistance to content but not to the level of consulting
Production
Content Publisher(s)Helped with publishing platform
Content Publishing Reviewer(s)Reviewed overall content and aesthetics on publishing platform
Technical
Course Publishing Engineer(s)Helped with the code for the technical aspects related to the specific course generation
Template Publishing EngineersCandace Savonen, Carrie Wright
Publishing Maintenance EngineerCandace Savonen
Technical Publishing StylistsCarrie Wright, Candace Savonen
Package Developers (Leanbuild)John Muschelli, Candace Savonen, Carrie Wright
Art and Design
Illustrator(s)Created graphics for the course
Figure Artist(s)Created figures/plots for course
Videographer(s)Filmed videos
Videography Editor(s)Edited film
Audiographer(s)Recorded audio
Audiography Editor(s)Edited audio recordings
Funding
Funder(s)Institution/individual who funded course including grant number
Funding StaffStaff members who help with funding
+

 

+
## ─ Session info ───────────────────────────────────────────────────────────────
+##  setting  value
+##  version  R version 4.3.2 (2023-10-31)
+##  os       Ubuntu 22.04.4 LTS
+##  system   x86_64, linux-gnu
+##  ui       X11
+##  language (EN)
+##  collate  en_US.UTF-8
+##  ctype    en_US.UTF-8
+##  tz       Etc/UTC
+##  date     2026-05-10
+##  pandoc   3.1.1 @ /usr/local/bin/ (via rmarkdown)
+## 
+## ─ Packages ───────────────────────────────────────────────────────────────────
+##  package     * version date (UTC) lib source
+##  bookdown      0.46    2025-12-05 [1] CRAN (R 4.3.2)
+##  bslib         0.6.1   2023-11-28 [1] RSPM (R 4.3.0)
+##  cachem        1.0.8   2023-05-01 [1] RSPM (R 4.3.0)
+##  chromote      0.5.1   2025-04-24 [1] CRAN (R 4.3.2)
+##  cli           3.6.5   2025-04-23 [1] CRAN (R 4.3.2)
+##  devtools      2.4.5   2022-10-11 [1] RSPM (R 4.3.0)
+##  digest        0.6.34  2024-01-11 [1] RSPM (R 4.3.0)
+##  dplyr         1.1.4   2023-11-17 [1] RSPM (R 4.3.0)
+##  ellipsis      0.3.2   2021-04-29 [1] RSPM (R 4.3.0)
+##  evaluate      1.0.5   2025-08-27 [1] CRAN (R 4.3.2)
+##  fansi         1.0.6   2023-12-08 [1] RSPM (R 4.3.0)
+##  fastmap       1.1.1   2023-02-24 [1] RSPM (R 4.3.0)
+##  fs            1.6.3   2023-07-20 [1] RSPM (R 4.3.0)
+##  generics      0.1.3   2022-07-05 [1] RSPM (R 4.3.0)
+##  gitcreds      0.1.2   2022-09-08 [1] RSPM (R 4.3.0)
+##  glue          1.7.0   2024-01-09 [1] RSPM (R 4.3.0)
+##  hms           1.1.3   2023-03-21 [1] RSPM (R 4.3.0)
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+##  [2] /usr/local/lib/R/library
+## 
+## ──────────────────────────────────────────────────────────────────────────────
+ + +
+
+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
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+ + + + + + + + + + + + + diff --git a/docs/no_toc/appendix-i-ontologies-vocabularies-taxonomies-and-standards.html b/docs/no_toc/appendix-i-ontologies-vocabularies-taxonomies-and-standards.html new file mode 100644 index 0000000..a03b149 --- /dev/null +++ b/docs/no_toc/appendix-i-ontologies-vocabularies-taxonomies-and-standards.html @@ -0,0 +1,306 @@ + + + + + + + Chapter 6 Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards | Clinical Data Analysis for Cancer Informatics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Chapter 6 Appendix I: Ontologies, Vocabularies, Taxonomies, and Standards

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+

6.1 SNOMED

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+

6.2 ICD10

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+

6.3 RxNorm

+
+
+

6.4 LOINC

+
+
+

6.5 Emerging standards such as FHIR

+
+
+

6.6 OMOP

+ +
+
+
+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
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+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
+
\ No newline at end of file diff --git a/docs/no_toc/assets/hideOutput.js b/docs/no_toc/assets/hideOutput.js new file mode 100644 index 0000000..245d55f --- /dev/null +++ b/docs/no_toc/assets/hideOutput.js @@ -0,0 +1,57 @@ +/*$(document).ready(function() { + + + // Copy Button + $chunks = $('pre.sourceCode > code.sourceCode'); + $chunks.each(function(i, val) { + $(this).prepend("").click(function() { + var $temp = $("",l.noCloneChecked=!!b.cloneNode(!0).lastChild.defaultValue}();var da=/^key/,ea=/^(?:mouse|pointer|contextmenu|drag|drop)|click/,fa=/^([^.]*)(?:\.(.+)|)/;function ga(){return!0}function ha(){return!1}function ia(){try{return d.activeElement}catch(a){}}function ja(a,b,c,d,e,f){var g,h;if("object"==typeof b){"string"!=typeof c&&(d=d||c,c=void 0);for(h in b)ja(a,h,c,d,b[h],f);return a}if(null==d&&null==e?(e=c,d=c=void 0):null==e&&("string"==typeof c?(e=d,d=void 0):(e=d,d=c,c=void 0)),e===!1)e=ha;else if(!e)return a;return 1===f&&(g=e,e=function(a){return 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OR this works:

+ + + + +
+

2.1.6 Citation examples

+

We can put citations at the end of a sentence like this (Allaire et al. 2021). +Or multiple citations Xie, Allaire, and Grolemund (2018).

+

but they need a ; separator (Allaire et al. 2021; Xie, Allaire, and Grolemund 2018).

+

In text, we can put citations like this Allaire et al. (2021).

+
+ + + +

References

+
+
+Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2021. Rmarkdown: Dynamic Documents for r. https://github.com/rstudio/rmarkdown. +
+
+Xie, Yihui, J. J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown. +
+
+
+
+
+ All illustrations CC-BY. +
+ All other materials CC-BY unless noted otherwise. +
+
+ + + + + + + + + + + + + + + + + + + + + + From 157f5e39ab314f08cc3b6b7a81e16c06e061c8c7 Mon Sep 17 00:00:00 2001 From: Sarah Teichman Date: Tue, 7 Jul 2026 10:44:11 -0700 Subject: [PATCH 11/11] Address spelling errors --- resources/dictionary.txt | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/resources/dictionary.txt b/resources/dictionary.txt index 5d61f70..82932f6 100644 --- a/resources/dictionary.txt +++ b/resources/dictionary.txt @@ -16,6 +16,7 @@ colorectal comorbidities Comorbidities CPT +CRF CRFs CUIs customizations @@ -48,9 +49,12 @@ McGrady MEM Modi Multivariable +ncbi NCI NDC +nih NIH's +nlm NLP OMOP ontologies @@ -60,6 +64,8 @@ Permanente personalization Ph PHM +pmc +PMC polypharmacy pre PROMs