Wallace Michael P, Moodie Erica E M
Department of Epidemiology, Biostatistics and Occupational Health McGill University, Montreal, Canada.
Biometrics. 2015 Sep;71(3):636-44. doi: 10.1111/biom.12306. Epub 2015 Apr 8.
Personalized medicine is a rapidly expanding area of health research wherein patient level information is used to inform their treatment. Dynamic treatment regimens (DTRs) are a means of formalizing the sequence of treatment decisions that characterize personalized management plans. Identifying the DTR which optimizes expected patient outcome is of obvious interest and numerous methods have been proposed for this purpose. We present a new approach which builds on two established methods: Q-learning and G-estimation, offering the doubly robust property of the latter but with ease of implementation much more akin to the former. We outline the underlying theory, provide simulation studies that demonstrate the double-robustness and efficiency properties of our approach, and illustrate its use on data from the Promotion of Breastfeeding Intervention Trial.
个性化医疗是健康研究中一个迅速发展的领域，其中利用患者层面的信息来指导其治疗。动态治疗方案（DTRs）是一种将个性化管理计划所特有的治疗决策序列形式化的方法。识别能优化患者预期治疗结果的DTR显然很有意义，为此已经提出了许多方法。我们提出了一种新方法，该方法基于两种既定方法：Q学习和G估计，具有后者的双重稳健性，但实施起来更类似于前者。我们概述了其基础理论，提供了模拟研究以证明我们方法的双重稳健性和效率特性，并举例说明了其在母乳喂养促进干预试验数据中的应用。
