title: Greedy outcome weighted tree learning of optimal personalized treatment rules.
abstract: We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a high-dimensional space. In the first step, we transform the treatment selection problem into a weighted classification problem that can utilize tree-based methods. In the second step, we adopt a newly proposed tree-based method, known as reinforcement learning trees, to detect features involved in the optimal treatment rules and to construct binary splitting rules. The method is further extended to right censored survival data by using the accelerated failure time model and introducing double weighting to the classification trees. The performance of the proposed method is demonstrated via simulation studies, as well as analyses of the Cancer Cell Line Encyclopedia (CCLE) data and the Tamoxifen breast cancer data.
authors.affiliation: University of Illinois at Urbana-Champaign, Champaign, Illinois, 61820, U.S.A.
authors.forename: Ruoqing
authors.initials: R
authors.lastname: Zhu
authors.affiliation: Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, U.S.A.
authors.forename: Ying-Qi
authors.initials: YQ
authors.lastname: Zhao
authors.affiliation: Vanderbilt University, Nashville, Tennessee, 37240, U.S.A.
authors.forename: Guanhua
authors.initials: G
authors.lastname: Chen
authors.affiliation: Yale University, New Haven, Connecticut, 06520, U.S.A.
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
authors.affiliation: Yale University, New Haven, Connecticut, 06520, U.S.A.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D006801:Humans; D010517:Periodontics
keywords: High-dimensional data; Optimal treatment rules; Personalized medicine; Reinforcement learning trees; Survival analysis; Tree-based method