光华讲坛——海外名家讲堂第 27 期

主题： New Approaches for Inference on Optimal Treatment Regimes

主讲人： 迈阿密大学WANG LAN教授

主持人： 统计学院林华珍教授

时间：2021年12月1日（周三）上午9：30-10：30

举办地点：腾讯会议号459-904-942

主办单位： 统计研究中心和统计学院 国际交流与合作处 科研处

主讲人简介：（须有英文介绍）

Dr. Lan Wang is a Professor in Department of Management Science at the Miami Herbert Business School of the University of Miami.Dr. Wang's research covers several interrelated areas: high-dimensional statistical learning, quantile regression, optimal personalized decision recommendation and survival analysis. She is also interested in interdisciplinary collaboration, driven by applications in healthcare, economics, engineering and other domains. Dr. Wang is an elected Fellow of the American Statistical Association, an elected Fellow of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. She is serving on the editorial boards of leading statistical journals Annals of Statistics and Journal of the American Statistical Associations.

Wang Lan,迈阿密大学迈阿密赫伯特商学院管理科学系教授。她的研究兴趣包括：高维统计学习，分位数回归，最佳个性化决策推荐和生存分析。她还对医疗保健、经济、工程和其他领域的应用驱动的跨学科合作感兴趣。她是美国统计学会（ASA）、美国数理统计协会（IMS）的elected Fellow，国际统计协会（ISI）的elected member。她是统计学顶级期刊AoS和JASA的编辑委员会成员。

内容简介：（须有英文介绍）

Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in precision medicine. We propose two new approaches to quantify uncertainty in optimal treatment regime estimation. First, we consider inference in the model-free setting, which does not require to specify an outcome regression model. Existing model-free estimators for optimal treatment regimes are usually not suitable for the purpose of inference, because they either have nonstandard asymptotic distributions or do not necessarily guarantee consistent estimation of the parameter indexing the Bayes rule due to the use of surrogate loss. We study a smoothed robust estimator that directly targets the parameter corresponding to the Bayes decision rule for optimal treatment regimes estimation. We verify that a resampling procedure provides asymptotically accurate inference for both the parameter indexing the optimal treatment regime and the optimal value function. Next, we consider the high-dimensional setting and propose a semiparametric model assisted approach for simultaneous inference. Simulations results and real data examples are used for illustration. (Joint work with Yunan Wu and Haoda Fu)

根据个体特征寻找最佳治疗方案（或一系列序贯治疗方案）在精准医疗中具有重要的应用。我们提出了两种新方法来量化最佳治疗方案估计中的不确定性。首先，我们考虑无模型设置中的推断，这不需要指定结果回归模型。用于最佳治疗方案的现有无模型估计通常不适用于推断，因为它们要么具有非标准渐近分布，要么由于使用了代理损失，不一定保证索引贝叶斯准则的参数的一致估计。我们研究了一个光滑的稳健估计器，它直接针对与贝叶斯决策规则对应的参数，以实现最佳治疗方案估计。我们验证了重采样程序为索引最佳治疗方案和最佳值函数的参数提供了渐近准确的推断。接下来，我们考虑高维设置，并提出一种半参数模型辅助方法进行同步推断。仿真结果和真实数据例子表现良好。（与Yunan Wu and Haoda Fu合作完成）