SA16 Enhancing Clinical Trial Diversity and Representation Through Data-Adaptive Targeted Learning: A Tale of Lurking Berkson Bias

Abstract

There is a ubiquitous need for enhanced diversity in clinical trials for generalizability. Diverse trial populations allow for more accurate predictions of the safety and efficacy of potential medicines with implications for equitable treatments. Further, the real-world data (RWD) collection process may result in biased estimates because of structural differences in access to healthcare, socioeconomic status, and cultural environments. Additionally, many traditional analytic methods can restrict cohorts and disproportionately exclude patient groups. Applying causal structural reasoning and data-adaptive targeted learning (TL) provides an efficient opportunity to bolster diversity and inclusion in trial analysis.