ArticleJournal of biomedical informatics2016
Multivariate analysis of the population representativeness of related clinical studies.
Article in Journal of biomedical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Clinical Trial Generalizability Assessment in the Big Data Era: A Review.Clinical and translational science · 2020Pooled it
- Computational strategic recruitment for representation and coverage studied in the All of Us Research Program.NPJ digital medicine · 2025Article
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- Quantifying representativeness in randomized clinical trials using machine learning fairness metrics.JAMIA open · 2021Article
- "Include me if you can"-reasons for low enrollment of pediatric patients in a psychopharmacological trial.Trials · 2021Article
- Novel stepwise approach to assess representativeness of a large multicenter observational cohort of tuberculosis patients: The example of RePORT Brazil.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Observational
- Reporting of demographic data and representativeness in machine learning models using electronic health records.Journal of the American Medical Informatics Association : JAMIA · 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
- 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 Validity of aAMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Prevalence of Multiple Chronic Conditions Among Older Adults in Florida and the United States: Comparative Analysis of the OneFlorida Data Trust and National Inpatient Sample.Journal of medical Internet research · 2018Article
- The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Comparing and Contrasting A Priori and A Posteriori Generalizability Assessment of Clinical Trials on Type 2 Diabetes Mellitus.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Correlating eligibility criteria generalizability and adverse events using Big Data for patients and clinical trials.Annals of the New York Academy of Sciences · 2017Article
- GIST 2.0: A scalable multi-trait metric for quantifying population representativeness of individual clinical studies.Journal of biomedical informatics · 2016Article
- Assessing the population representativeness of colorectal cancer treatment clinical trials.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2016Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.
methodsWe extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data.
resultsAccording to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment.
conclusionsmGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.
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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.