<i>Who Are We Missing?</i>: A Principled Approach to Characterizing the Underrepresented Population

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.

随机对照试验（Randomized Controlled Trials, RCTs）是探究因果效应的基石，但由于效应异质性与代表性不足问题，将研究推论推广至目标人群仍面临诸多挑战。本文聚焦于随机对照试验中识别并刻画代表性不足亚组这一关键问题，提出了一种用于优化目标人群以提升研究推论外推性的全新框架。我们提出了一种基于优化的方法——最优树拉什蒙集合（Rashomon Set of Optimal Trees, ROOT），用于刻画代表性不足的人群亚组。该方法通过最小化目标平均治疗效应估计量的方差，优化目标亚群的分布，从而确保治疗效应估计具备更高的精准度。值得注意的是，ROOT能够生成代表性不足人群的可解释性特征，助力研究人员开展高效的学术沟通。通过合成数据实验验证可知，相较于其他同类方法，本方法在精准度与可解释性上均表现更优。我们将所提方法应用于将“激动剂替代疗法起始治疗（Starting Treatment with Agonist Replacement Therapies, START）”试验（该试验探究了药物治疗阿片类使用障碍的有效性）的推论推广至以“治疗事件数据集：入院数据（Treatment Episode Dataset: Admissions, TEDS-A）”为代表的真实世界人群。通过ROOT优化目标人群，本框架提供了一种系统化的方法，能够提升决策精准度，并为不同人群背景下的未来临床试验提供参考依据。本文的补充材料已在线发布，其中包含可用于复现研究成果的标准化材料说明。