Time：2019/6/14   10:00-11:00

Location：Mingde Main Building 1016

Abstract:

The ability to predict individualized treatment effects (ITEs) based on a given patient`s profile is essential for personalized medicine. The prediction of ITEs enables the comparison of the effectiveness of two treatment procedures for a specific individual. In this talk, we discuss a hypothesis testing approach to choosing between two available treatments for a given individual in the framework of high-dimensional linear models. The methodological novelty is the development of a testing procedure with the type-I error uniformly controlled for any future high-dimensional observation, while the existing methods can only handle certain specific forms of covariates observation. We introduce the optimality framework for hypothesis testing in high dimensions from both minimaxity and adaptivity perspectives and establish the optimality of the proposed procedure. The method can be extended to conduct statistical inference for general linear contrasts, including both average treatment effect and prediction. The procedure is further illustrated through an analysis of EHR data from patients with rheumatoid arthritis.