报告题目：New Statistical Learning Methods for Estimating Optimal Dynamic
Treatment Regimes

报告人：Donglin Zeng,  University of North Carolina at Chapel Hill

时  间：2014年7月4日    下午4:00―5:00

地  点：东区管理科研楼  365英国上市官网1218室

内容提要：
Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example Q-learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with $Q$-learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation.

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