CAPITAL: Optimal subgroup identification via constrained policy tree search.

ABSTRACT: Personalized medicine, a paradigm of medicine tailored to a patient's characteristics, is an increasingly attractive field in health care. An important goal of personalized medicine is to identify a subgroup of patients, based on baseline covariates, that benefits more from the targeted treatment than other comparative treatments. Most of the current subgroup identification methods only focus on obtaining a subgroup with an enhanced treatment effect without paying attention to subgroup size. Yet, a clinically meaningful subgroup learning approach should identify the maximum number of patients who can benefit from the better treatment. In this article, we present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients, and in the meantime, achieves the pre-sp

SUBMITTER: Cai H 

PROVIDER: S-EPMC9544117 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature