【统计大讲堂】张拔群:C-learning,一种新的动态优化分类框架
(统计学院发布于:2017-11-10 17:50:36)

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

摘要: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.

介绍: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。


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