ArticleJAMIA open2021
Quantifying representativeness in randomized clinical trials using machine learning fairness metrics.
Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 26 citations in OpenAlex.
- Nutrition with a patent ductus arteriosus: feast, feed, or famine?Pediatric research · 2026Article
- Reach and representativeness within a rural randomized controlled trial designed to improve community health through civic engagement for built environment change in Texas and New York.Wellbeing, space and society · 2026Article
- Computational strategic recruitment for representation and coverage studied in the All of Us Research Program.NPJ digital medicine · 2025Article
- POP-REFINE: A Comprehensive Framework for Evaluating and Optimizing Representativeness in Clinical Trials.Clinical pharmacology and therapeutics · 2025Article
- Enhancing neuro-oncology care through equity-driven applications of artificial intelligence.Neuro-oncology · 2024Review
- Toward real-world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials.Health care science · 2024Article
- Quantitative evaluation of the impact of relaxing eligibility criteria on the risk-benefit profile of drugs for lung cancer based on real-world data.Thoracic cancer · 2024Article
- A novel approach to assessing disparity in representativeness of clinical trial participants within a large midwestern healthcare system.Contemporary clinical trials communications · 2024Article
- Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Fair Canonical Correlation Analysis.Advances in neural information processing systems · 2023Article
- Representativeness in health research studies: an audit of Greater Manchester Clinical Research Network studies between 2016 and 2021.BMC medicine · 2023Article
- Positive Psychology Interventions in Medical Populations: Critical Issues in Intervention Development, Testing, and Implementation.Affective science · 2023Review
- Demographic and socioeconomic determinants of access to care: A subgroup disparity analysis using new equity-focused measurements.PloS one · 2023Article
- Artificial Intelligence Applied to clinical trials: opportunities and challenges.Health and technology · 2023Review
- Framework for Research in Equitable Synthetic Control Arms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
- A Representativeness-informed Model for Research Record Selection from Electronic Medical Record Systems.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- The Problem of Fairness in Synthetic Healthcare Data.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe help identify subpopulations underrepresented in randomized clinical trials (RCTs) cohorts with respect to national, community-based or health system target populations by formulating population representativeness of RCTs as a machine learning (ML) fairness problem, deriving new representation metrics, and deploying them in easy-to-understand interactive visualization tools. MATERIALS AND
methodsWe represent RCT cohort enrollment as random binary classification fairness problems, and then show how ML fairness metrics based on enrollment fraction can be efficiently calculated using easily computed rates of subpopulations in RCT cohorts and target populations. We propose standardized versions of these metrics and deploy them in an interactive tool to analyze 3 RCTs with respect to type 2 diabetes and hypertension target populations in the National Health and Nutrition Examination Survey.
resultsWe demonstrate how the proposed metrics and associated statistics enable users to rapidly examine representativeness of all subpopulations in the RCT defined by a set of categorical traits (eg, gender, race, ethnicity, smoking status, and blood pressure) with respect to target populations. DISCUSSION: The normalized metrics provide an intuitive standardized scale for evaluating representation across subgroups, which may have vastly different enrollment fractions and rates in RCT study cohorts. The metrics are beneficial complements to other approaches (eg, enrollment fractions) used to identify generalizability and health equity of RCTs.
conclusionBy quantifying the gaps between RCT and target populations, the proposed methods can support generalizability evaluation of existing RCT cohorts. The interactive visualization tool can be readily applied to identified underrepresented subgroups with respect to any desired source or target populations.
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What OpenQuestion holds
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.