Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects

• 成果类型: Article
• 署名作者: Shi, Chengchun; Song, Rui; Lu, Wenbin; Fu, Bo
• 署名单位: North Carolina State University; Fudan University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12273
• 发表日期: 2018
• 页码: 681-702
• 关键词: randomized controlled-trial; Dynamic Treatment Regimes; large-scale data; rheumatoid-arthritis; metaanalysis; inference; therapy

摘要

A salient feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also in the way that they respond to treatment. Optimal individualized treatment regimes are developed to select effective treatments based on patient's heterogeneity. However, the optimal treatment regime might also vary for patients across different subgroups. We mainly consider patients' heterogeneity caused by groupwise individualized treatment effects assuming the same marginal treatment effects for all groups. We propose a new maximin projection learning method for estimating a single treatment decision rule that works reliably for a group of future patients from a possibly new subpopulation. Based on estimated optimal treatment regimes for all subgroups, the proposed maximin treatment regime is obtained by solving a quadratically constrained linear programming problem, which can be efficiently computed by interior point methods. Consistency and asymptotic normality of the estimator are established. Numerical examples show the reliability of the methodology proposed.