Quantifying representativeness in RCTs using ML fairness metrics - Data and codes

The \"Quantifying representativeness in RCTs using ML fairness metrics - Data and codes\" is used to quantify representativeness in randomized clinical trials (RCTs) and provide insights to improve the clinical trial equity and health equity. We developed RCT representativeness metrics based on Machine Learning (ML) Fairness Research. Visualizations and statistical tests based on proposed metrics enable researchers and physicians to rapidly visualize and assess subgroup representation in RCTs. The approach enables users to determine underrepresentation, absence, or other misrepresentation of subgroups indicating potential limitations of RCTs. The method could help support generalizability evaluation of existing RCT cohorts, enrollment target decisions for new RCTs (if eligibility criteria are included), and monitoring of RCT enrollment, ultimately contributing to more equitable public health outcomes. We apply the proposed RCT representativeness metrics to three landmark clinical trials r...