Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data

• 成果类型: Article
• 署名作者: Xue, Fei; Zhang, Yanqing; Zhou, Wenzhuo; Fu, Haoda; Qu, Annie
• 署名单位: University of Pennsylvania; Yunnan University; University of Illinois System; University of Illinois Urbana-Champaign; Eli Lilly; University of California System; University of California Irvine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1862671
• 发表日期: 2022
• 页码: 1438-1451
• 关键词: multiclass

摘要

An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed to maximize the expected survival time under a binary treatment framework, the proposed method solves the multicategory treatment problem given multiple stages for censored data. Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. In theory, we establish Fisher consistency and provide the risk bound for the proposed estimator under regularity conditions. Our numerical studies show that the proposed method outperforms competing methods in terms of maximizing the conditional survival probability. We apply the proposed method to two real datasets: Framingham heart study data and acquired immunodeficiency syndrome clinical data. for this article are available online.