Quantifying representativeness in randomized clinical trials using machine learning fairness metrics.

ABSTRACT:

Objective

We 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 methods

We 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 RC

SUBMITTER: Qi M 

PROVIDER: S-EPMC8460438 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature