Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population
This work addresses the challenge of extending RCT inferences to real-world populations for researchers and policymakers, though it is incremental as it builds on existing methods for effect heterogeneity.
The paper tackles the problem of identifying underrepresented subgroups in randomized controlled trials (RCTs) to improve generalizability, proposing the ROOT framework that optimizes target populations for more precise treatment effect estimates, as demonstrated with synthetic data and an application to opioid use disorder trials.
Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our paper addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), to characterize underrepresented groups. ROOT optimizes the target subpopulation distribution by minimizing the variance of the target average treatment effect estimate, ensuring more precise treatment effect estimations. Notably, ROOT generates interpretable characteristics of the underrepresented population, aiding researchers in effective communication. Our approach demonstrates improved precision and interpretability compared to alternatives, as illustrated with synthetic data experiments. We apply our methodology to extend inferences from the Starting Treatment with Agonist Replacement Therapies (START) trial -- investigating the effectiveness of medication for opioid use disorder -- to the real-world population represented by the Treatment Episode Dataset: Admissions (TEDS-A). By refining target populations using ROOT, our framework offers a systematic approach to enhance decision-making accuracy and inform future trials in diverse populations.