一、主题：Regularized Outcome Weighted Subgroup Identification for Differential Treatment Effects

二、主讲人：王思鉴，威斯康辛大学统计学院助理教授。2003年本科毕业于清华大学应用数学系，2005年获得密西根大员工物统计硕士学位，2008年获得密西根大员工物统计博士学位。研究兴趣包括：大数据/高频数据分析、统计学习、生物信息学、基因组学、纵向数据分析、遗漏数据分析、生存数据分析等。研究成果在Bioinformatics、Biometrics、Annals of Applied Statistics、Annals of Statistics、Canadian Journal of Statistics等国际高水平统计学刊物发表论文十余篇。

三、时间：12月25日（周三），16：00—17：00

四、地点：公司主教楼913会议室

五、主持人：刘向丽，靠谱的十大网投实体平台(中国)有限公司教授

Abstract: To facilitate comparative treatment selection when there is substantial heterogeneity of treatment effectiveness, it is important to identify subgroups that exhibit differential treatment effects. Existing approaches model outcomes directly and then define subgroups according to treatment and covariates interaction. However outcomes are affected by both the covariate-treatment interactions and covariate main effects. Consequently mis-specification of the main effects interferes with the covariate-treatment interaction estimation thus impedes valid predictive variable identification. We propose a method that approximates a target function whose value directly reflects correct treatment assignment for patients. This can disconnect the covariate main effects from the covariate- treatment interactions. The function uses patient outcomes as weights instead as modeling targets. Consequently, our method can deal with binary, continuous, time-to-event, and possibly contaminated outcomes in the same fashion. We first focus on identifying only directional estimates from linear rules that characterize important subgroups. We further consider estimation of differential comparative treatment effects for identified subgroups. We demonstrate the advantages of our method in simulation studies and in an analysis of two real data sets.