A sparse random projection-based test for overall qualitative treatment effects

Shi, Chengchun ORCID: 0000-0001-7773-2099, Lu, Wenbin and Song, Rui (2019) A sparse random projection-based test for overall qualitative treatment effects. Journal of the American Statistical Association. ISSN 0162-1459

Text (OQTEv4unblinded) - Accepted Version Download (167kB)

• Author

Abstract

In contrast to the classical “one-size-fits-all” approach, precision medicine proposes the customization of individualized treatment regimes to account for patients’ heterogeneity in response to treatments. Most of existing works in the literature focused on estimating optimal individualized treatment regimes. However, there has been less attention devoted to hypothesis testing regarding the existence of overall qualitative treatment effects, especially when there are a large number of prognostic covariates. When covariates do not have qualitative treatment effects, the optimal treatment regime will assign the same treatment to all patients regardless of their covariate values. In this article, we consider testing the overall qualitative treatment effects of patients’ prognostic 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, we show the asymptotic power function of our test statistic is asymptotically the same as the “oracle” test statistic which is constructed based on the “optimal” projection matrix. Simulation studies and real data applications validate our theoretical findings. Supplementary materials for this article are available online.

Item Type:	Article
Official URL:	https://amstat.tandfonline.com/toc/uasa20/current
Additional Information:	© 2019 American Statistical Association
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	15 Oct 2019 12:12
Last Modified:	01 Nov 2024 17:24
URI:	http://eprints.lse.ac.uk/id/eprint/102107

Actions (login required)

View Item