3月6日迈阿密大学Dr. Lan Wang来中心讲座预告

讲座题目：New Approaches for Inference on Optimal Treatment Regimes

主 讲 人：Lan Wang (Department of Management Science, Miami Herbert Business School, University of Miami)

讲座时间：2022年3月6日（周日）上午10:00-11:00

地点：腾讯会议  ID 892-203-553

https://meeting.tencent.com/dm/Nr6ZZvdLrKn2

主讲人简介：

Dr. Lan Wang is a tenured Professor in the Department of Management Science at the Miami Herbert Business School of the University of Miami, with a secondary appointment as Professor of Public Health Sciences at the Miller School of Medicine, University of Miami. She currently serves as the Co-Editor for Annals of Statistics (2022-2024), jointly with Professor Enno Mammen. 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, business, economics, 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 was the associate editor for several leading statistical journals: Journal of the American Statistical Associations, Annals of Statistics, Journal of the Royal Statistical Society, and Biometrics.

讲座摘要：

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 specifying 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. Simulation results and real data examples are used for illustration.