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Activity Number: 309 - Statistical Topics in Precision Medicine
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #323454 View Presentation
Title: Hypothesis testings on high-dimensional individualized treatment rules
Author(s): Young-Geun Choi* and Yang Ning and Yingqi Zhao
Companies: Fred Hutchinson Cancer Research Center and Cornell University and Fred Hutchinson Cancer Research Center
Keywords: Individualized treatment rules ; outcome weighted learning (O-learning) ; penalized M-estimtion ; hypothesis testing ; high-dimension
Abstract:

Individualized treatment rules (ITR) assign treatments according to different patient's characteristics. Despite recent advances on the estimation of ITRs, much less attention has been given to uncertainty assessments for the estimated rules. We propose a hypothesis testing procedure for the estimated ITRs from a general framework that directly optimizes overall treatment benefit. Specifically, we construct a local test for testing low dimensional components of high-dimensional linear decision rules. Our test extends the decorrelated score test proposed in Nang and Liu (2017) and is valid no matter whether model selection consistency for the true parameters holds or not. The proposed methodology is illustrated with numerical study and data examples.


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