Estimating Individualized Treatment Rules Using Outcome Weighted Learning
Yingqi Zhao,
Donglin Zeng,
A. John Rush and
Michael R. Kosorok
Journal of the American Statistical Association, 2012, vol. 107, issue 499, 1106-1118
Abstract:
There is increasing interest in discovering individualized treatment rules (ITRs) for patients who have heterogeneous responses to treatment. In particular, one aims to find an optimal ITR that is a deterministic function of patient-specific characteristics maximizing expected clinical outcome. In this article, 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 ITR 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.
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (127)
Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2012.695674 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:107:y:2012:i:499:p:1106-1118
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20
DOI: 10.1080/01621459.2012.695674
Access Statistics for this article
Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson
More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst (chris.longhurst@tandf.co.uk).