Often in public health, we are interested in the treatment effect of an intervention on a population that is systemically different from the experimental population the intervention was originally evaluated in. When treatment effect heterogeneity is present in a randomized controlled trial, generalizing the treatment effect from this experimental population to a target population of interest is a complex problem; it requires the characterization of both the treatment effect heterogeneity and the baseline covariate mismatch between the two populations. Despite the importance of this problem, the literature for variable selection in this context is limited. In this paper, we present a Group LASSO-based approach to variable selection in the context of treatment effect generalization, with an application to generalize the treatment effect of very low nicotine content cigarettes to the overall U.S. smoking population.

翻译：在公共卫生领域，我们常关注某项干预措施对目标人群的治疗效应，而该人群与干预措施最初评估时所基于的实验人群存在系统性差异。当随机对照试验中存在治疗效应异质性时，将实验人群的治疗效应推广至目标人群是一个复杂问题：这需要同时刻画治疗效应异质性以及两个人群之间基线协变量的不匹配性。尽管该问题具有重要意义，但在此背景下进行变量选择的文献尚显不足。本文提出一种基于Group LASSO的变量选择方法用于治疗效应泛化，并将其应用于将极低尼古丁含量卷烟的治疗效应推广至全美吸烟人群。