【摘 要】
：
It is important to stratify patient population such that the treatment effect is different for different subgroups of patients.The stratification can be use
【机 构】
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Stanford University Palo Alto, CA 94303, USA
【出 处】
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The Third IMS-China International Conference on Statistics a
论文部分内容阅读
It is important to stratify patient population such that the treatment effect is different for different subgroups of patients.The stratification can be used to select the optimal treatment for individual patients in a given subgroup.Statistically speaking, it amount to identify treatment and covariate interactions.This is a challenging task especially when the dimension of covariate is high.We propose a novel approach to directly estimate the covariate-treatment interaction without the need for modeling the main effect.Therefore, the method is very appealing for cases where the high-dimensional covariate such as gene expression levels is involved.We provide some theoretical justifications from casual inference perspective and illustrate the proposet with real data examples.
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