题目：C-learning: a New Classification Framework to Estimate Optimal Dynamic Treatment Regimes
主讲：张拔群
时间：2017年11月13日（星期一） 14:30-15:30
地点：明德主楼 1030会议室
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual`s own available characteristics and treatment history up to that point.We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient`s characteristics and treatment history to improves performance, hence enjoying the advantages of both the traditional outcome regression based methods (Q-and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies.
简介：
张拔群，上海财经大学统计与管理学院副教授，2006年本科毕业于南开大学，2012年博士毕业于北卡州立大学。主要研究方向：生物医学统计，精准医疗。在国际期刊Biometrika，Biometrics，Bioinformation已发表学术论文多篇，其中入选ESI高被引论文一篇。