ArticleApplied clinical informatics2014
A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records.
Article in Applied clinical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Clinical Trial Generalizability Assessment in the Big Data Era: A Review.Clinical and translational science · 2020Pooled it
- Characterizing treatment pathways at scale using the OHDSI network.Proceedings of the National Academy of Sciences of the United States of America · 2016Pooled it
- Visualizing Baseline Eligibility Across Locally Advanced or Metastatic Urothelial Cancer Trials and Real-World Cohorts.International journal of urology : official journal of the Japanese Urological Association · 2026Article
- Computational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference.Circulation. Cardiovascular quality and outcomes · 2025Article
- The challenges of integrating diverse data sources: A case study in major depression.Health services & outcomes research methodology · 2024Article
- Longitudinal natural history studies based on real-world data in rare diseases: Opportunity and a novel approach.Molecular genetics and metabolism · 2024Review
- A review of research on eligibility criteria for clinical trials.Clinical and experimental medicine · 2023Review
- Assessing Equitable Recruitment in a Digital Health Trial for Asthma.Applied clinical informatics · 2023Article
- Article
- A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data.Applied clinical informatics · 2021Article
- A knowledge base of clinical trial eligibility criteria.Journal of biomedical informatics · 2021Article
- Contemporary use of real-world data for clinical trial conduct in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Using Real-World Data to Rationalize Clinical Trials Eligibility Criteria Design: A Case Study of Alzheimer's Disease Trials.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Assessing the Use and Perception of Dietary Supplements Among Obese Patients with National Health and Nutrition Examination Survey.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables.Frontiers in pharmacology · 2020Review
- How and when informative visit processes can bias inference when using electronic health records data for clinical research.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Patient Engagement in Medical Research Among Older Adults: Analysis of the Health Information National Trends Survey.Journal of medical Internet research · 2019Article
- Learning Personalized Treatment Rules from Electronic Health Records Using Topic Modeling Feature Extraction.Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics · 2019Article
- An outcome model approach to transporting a randomized controlled trial results to a target population.Journal of the American Medical Informatics Association : JAMIA · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveTo improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example.
methodsOur data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values.
resultsGreater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75.
conclusionsWe contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generalizability of clinical trials and for defining population-representative clinical trial eligibility criteria.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.