嘉宾简介Biography
Xiao-Hua Zhou is a PKU Endowed Chair Professor at Peking University, the Chair of the Department of Biostatistics at Peking University, and the Director of the Biostatistics and Bioinformatics Research of Peking University Beijing International Center for Mathematics Research. He is an elected Fellow of AAAS, ASA, and IMS. He is an expert in the Recruitment Program of Global Experts, and was a winner of the NSFC Fund for Distinguished Young Scholars (overseas). Between 2003 and 2018, he served as a full professor at the University of Washington. He was the P.I. for many international funded projects. Also, he has published more than 260 SCI papers on causal inference, statistical modeling related methods and applications in medical research, of which 120 papers ranked Q1 by JCR, with a total citation rate of more than 12,000. He has won the US Federal Government Research Career Scientist Achievement Award, the Mitchell Award from the International Bayesian Society for developing a new theory and method of causal inference and sensitivity analysis, and the 2015 Chinese Science and Mathematics Outstanding Paper Award for proposing the use of personalized treatment effect curve in the selection theory of the optimal personalized treatment plan.
报告简介 Abstract
In this talk, I introduce a new semi-parametric modeling method for heterogeneous treatment effect estimation and individualized treatment selection using a covariate-specific treatment effect (CSTE) curve with high-dimensional covariates. The proposed method is quite flexible to depict both local and global associations between the treatment and baseline covariates, and thus is robust against model mis-specification in the presence of high-dimensional covariates. We also establish the theoretical properties of our proposed procedure. I will further illustrate the performance of the proposed method by simulation studies and analysis of a real data example. This is a joint work with Drs. Guo and Ma at University of California at Riverside.
讲座海报Poster
