|Title||Estimating Individualized Treatment Rules Using Outcome Weighted Learning.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Zhao, Yingqi, Donglin Zeng, John A Rush, and Michael R. Kosorok|
|Journal||J Am Stat Assoc|
|Date Published||2012 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 Journal||J Am Stat Assoc|
|Original Publication||Estimating individualized treatment rules using outcome weighted learning.|
|PubMed Central ID||PMC3636816|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA082659 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
Estimating Individualized Treatment Rules Using Outcome Weighted Learning.