Estimating Individualized Treatment Rules Using Outcome Weighted Learning.

Journal Article (Journal Article)

There is increasing interest in discovering individualized treatment rules for patients who have heterogeneous responses to treatment. In particular, one aims to find an optimal individualized treatment rule which is a deterministic function of patient specific characteristics maximizing expected clinical outcome. In this paper, we first show that estimating such an optimal treatment rule is equivalent to a classification problem where each subject is weighted proportional to his or her clinical outcome. We then propose an outcome weighted learning approach based on the support vector machine framework. We show that the resulting estimator of the treatment rule is consistent. We further obtain a finite sample bound for the difference between the expected outcome using the estimated individualized treatment rule and that of the optimal treatment rule. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data.

Full Text

Duke Authors

Cited Authors

  • Zhao, Y; Zeng, D; Rush, AJ; Kosorok, MR

Published Date

  • September 1, 2012

Published In

Volume / Issue

  • 107 / 449

Start / End Page

  • 1106 - 1118

PubMed ID

  • 23630406

Pubmed Central ID

  • PMC3636816

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.2012.695674


  • eng

Conference Location

  • United States