CONCORDANCE AND VALUE INFORMATION CRITERIA FOR OPTIMAL TREATMENT DECISION

• 成果类型: Article
• 署名作者: Shi, Chengchun; Song, Rui; Lu, Wenbin
• 署名单位: University of London; London School Economics & Political Science; North Carolina State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/19-AOS1908
• 发表日期: 2021
• 页码: 49-75
• 关键词: nonconcave penalized likelihood; variable selection; u-processes; inference

摘要

Personalized medicine is a medical procedure that receives considerable scientific and commercial attention. The goal of personalized medicine is to assign the optimal treatment regime for each individual patient, according to his/her personal prognostic information. When there are a large number of pretreatment variables, it is crucial to identify those important variables that are necessary for treatment decision making. In this paper, we study two information criteria: the concordance and value information criteria, for variable selection in optimal treatment decision making. We consider both fixed-p and high dimensional settings, and show our information criteria are consistent in model/tuning parameter selection. We further apply our information criteria to four estimation approaches, including robust learning, concordance-assisted learning, penalized A-learning and sparse concordance-assisted learning, and demonstrate the empirical performance of our methods by simulations.