摘要： 

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.

报告人简介：Sijian Wang, Associate Professor, Department of Biostatistics & Medical Informatics and Department of Statistics. Research fields: High-dimensional data analysis, variable selection and model selection, survival analysis, longitudinal data analysis, bioinformatics, machine learning and data mining, statistical modeling.