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20171113 张拔群:C-learning: a New Classification Framework to Estimate Optimal Dynamic Treatment Regimes
时间:2017-11-13

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.

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BiometrikaBiometricsBioinformation已发表学术论文多篇,其中入选ESI高被引论文一篇。