Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions

• 成果类型: Article
• 署名作者: Zhang, Baqun; Tsiatis, Anastasios A.; Laber, Eric B.; Davidian, Marie
• 署名单位: Renmin University of China; North Carolina State University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ast014
• 发表日期: 2013
• 页码: 681694
• 关键词: regression; inference; DESIGN; models

摘要

A dynamic treatment regime is a list of sequential decision rules for assigning treatment based on a patient's history. Q- and A-learning are two main approaches for estimating the optimal regime, i.e., that yielding the most beneficial outcome in the patient population, using data from a clinical trial or observational study. Q-learning requires postulated regression models for the outcome, while A-learning involves models for that part of the outcome regression representing treatment contrasts and for treatment assignment. We propose an alternative to Q- and A-learning that maximizes a doubly robust augmented inverse probability weighted estimator for population mean outcome over a restricted class of regimes. Simulations demonstrate the method's performance and robustness to model misspecification, which is a key concern.