Estimating Individualized Treatment Rules Using Outcome Weighted Learning

• Published in: Journal of the American Statistical Association Vol. 107; no. 499; pp. 1106 - 1118
• Main Authors: Zhao, Yingqi, Zeng, Donglin, Rush, A. John, Kosorok, Michael R
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis Group 01.09.2012; Taylor & Francis Ltd
• Subjects: Approximation; Bayes classifier; Bayesian analysis; Clinical outcomes; Clinical trials; Consistent estimators; Cross-validation; data analysis; Depression; Dynamic treatment regime; Equivalence; Estimation; Estimation methods; Health outcomes; Individualized instruction; Individualized treatment rule; Learning; Mathematical functions; Modeling; Outcomes of education; Patients; Risk bound; RKHS; Sample size; Simulation; Statistical methods; Statistics; support vector machines; Theory and Methods; Weighted support vector machine; RKHS; Weighted Support Vector Machine; Risk Bound; Individualized Treatment Rule; Dynamic Treatment Regime; Bayes Classifier; Cross Validation
• ISSN: 1537-274X; 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2012.695674

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.