Estimating Individualized Treatment Rules Using Outcome Weighted Learning.

TitleEstimating Individualized Treatment Rules Using Outcome Weighted Learning.
Publication TypeJournal Article
Year of Publication2012
AuthorsZhao, Yingqi, Donglin Zeng, John A Rush, and Michael R. Kosorok
JournalJ Am Stat Assoc
Date Published2012 Sep 01

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.

Alternate JournalJ Am Stat Assoc
Original PublicationEstimating individualized treatment rules using outcome weighted learning.
PubMed ID23630406
PubMed Central IDPMC3636816
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA082659 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States