Who Are We Missing?: A Principled Approach to Characterizing the Underrepresented Population

成果类型:
Article; Early Access
署名作者:
Parikh, Harsh; Ross, Rachael K.; Stuart, Elizabeth; Rudolph, Kara E.
署名单位:
Johns Hopkins University; Columbia University
刊物名称:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN:
0162-1459
DOI:
10.1080/01621459.2025.2495319
发表日期:
2025
关键词:
external validity trials buprenorphine/naloxone methadone
摘要:
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 article 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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.