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Activity Number: 139 - Precision Medicine in High-Dimensional Settings
Type: Invited
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Association of Health Services Research
Abstract #300136
Title: A Sparse Random Projection-Based Test for Overall Qualitative Treatment Effects
Author(s): Chengchun Shi* and Wenbin Lu and Rui Song
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Keywords: High dimensional testing; Optimal treatment regime; Precision medicine; Qualitative treatment effects; Sparse random projection

In contrast to the classical "one size ts all" approach, precision medicine proposes the customization of individualized treatment regimes to account for patients' heterogeneity in response to treatments. In the literature, there has been less attention devoted to hypothesis testing regarding the existence of overall qualitative treatment effects, especially when there is a large number of prognostic covariates. When covariates don't have qualitative treatment effects, the optimal treatment regime will assign the same treatment to all patients regardless of their covariate values. In this paper, we consider testing the overall qualitative treatment effects of patients' covariates in a high dimensional setting. We propose a sample splitting method to construct the test statistic, based on a nonparametric estimator of the contrast function. When the dimension of covariates is large, we construct the test based on sparse random projections of covariates into a low-dimensional space. We prove the consistency of our test statistic. In the regular cases, the asymptotic power function of our test is the same as the oracle test which is constructed based on the optimal projection matrix.

Authors who are presenting talks have a * after their name.

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