Article

Quantifying representativeness in randomized clinical trials using machine learning fairness metrics

2021-06-28

Abstract excerpt

<h4>Objective</h4> We formulate population representativeness of randomized clinical trials (RCTs) as a machine learning (ML) fairness problem, derive new representation metrics, and deploy them in visualization tools which help users identify subpopulations that are underrepresented in RCT cohorts with respect to national, community-based or health system target populations. <h4>Materials and Methods</h4> We repr...

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Literature Corpus work: 11a8f2da-2095-5825-9f5d-a1f7ae3effb1
DOI: 10.1101/2021.06.23.21259272

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