统计与管理学院2016年学术报告第21期
【主 题】C-learning: a New Classification Framework to Estimate Optimal Dynamic Treatment Regimes
【报告人】 张拔群 博士
中国人民大学
【时 间】 2016年5月27日（星期五）09:00－10:00
【地 点】 上海财经大学统计与管理学院大楼1208室
【摘 要】A dynamic treatment regime is a set of decision rules, each corresponding to a decision point, 10 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 novel, powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage till 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 dramatically 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.
【邀请人】 周勇
