Estimating Individualized Treatment Rules Using Outcome Weighted Learning

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.

KEY WORDS:

• Bayes classifier
• Cross-validation
• Dynamic treatment regime
• Individualized treatment rule
• Risk bound
• RKHS
• Weighted support vector machine

Acknowledgments

The first, second, and fourth authors were partially funded by NCI Grant P01 CA142538. The authors thank the editor, associate editor, and referees for their helpful comments.