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

二、主講人：王思鑒，威斯康辛大學統計學院助理教授。2003年本科畢業于清華大學應用數學系，2005年獲得密西根大學生物統計碩士學位，2008年獲得密西根大學生物統計博士學位。研究興趣包括：大數據/高頻數據分析、統計學習、生物信息學、基因組學、縱向數據分析、遺漏數據分析、生存數據分析等。研究成果在Bioinformatics、Biometrics、Annals of Applied Statistics、Annals of Statistics、Canadian Journal of Statistics等國際高水平統計學刊物發表論文十餘篇。

三、時間：12月25日（周三），16：00—17：00

四、地點：bevictor伟德官网主教樓913會議室

五、主持人：劉向麗，bevictor伟德官网教授

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.