Data Integration for Sub-Group Level Effect Estimation in Clinical Trials

Thomas M. Menino Convention & Exhibition Center 

In randomized controlled trials (RCTs), leveraging summary information from other RCTs is a powerful strategy for estimation of subgroup-level conditional average treatment effect (CATE). However, most methods have not addressed the common setting where publicly available external estimates—such as single-factor CATEs (e.g., effects stratified by sex)— can be leveraged to estimate the finer subgroups of interest (e.g., sex-by-race interactions). To address this gap, we introduce a novel James–Stein (JS)–type estimator that uses coarser external subgroup summaries while accommodating potential violations of compatibility between external and internal estimates. We theoretically show the proposed estimator uniformly dominates the unconstrained estimator based solely on internal data, in terms of the mean squared error of the target CATE vectors, even under departures of the compatibility assumption. We apply our data integration method to a post hoc intersectional CATE analysis by sex and race in a tirzepatide weight-loss trial (SURMOUNT-1), incorporating sex-specific and race-specific effect estimates from two prior semaglutide trials (STEP 1 and STEP 2).