Real-world Application of Racial and Ethnic Imputation and Cohort Balancing Techniques to Deliver Equitable Clinical Trial Recruitment.

Abstract

Enhancing diversity and inclusion in clinical trial recruitment, especially for historically marginalized populations including Black, Indigenous, and People of Color individuals, is essential. This practice ensures that generalizable trial results are achieved to deliver safe, effective, and equitable health and healthcare. However, recruitment is limited by two inextricably linked barriers - the inability to recruit and retain enough trial participants, and the lack of diversity amongst trial populations whereby racial and ethnic groups are underrepresented when compared to national composition. To overcome these barriers, this study describes and evaluates a framework that combines 1) probabilistic and machine learning models to accurately impute missing race and ethnicity fields in real-world data including medical and pharmacy claims for the identification of eligible trial participants, 2) randomized controlled trial experimentation to deliver an optimal patient outreach strategy, and 3) stratified sampling techniques to effectively balance cohorts to continuously improve engagement and recruitment metrics.