|
Abstract:
|
Due to patient's heterogeneous response to treatment, there is a growing interest in developing novel and efficient statistical methods in estimating individualized treatment rules (ITRs). The central idea is to recommend treatment according to patient characteristics, and the optimal ITR is the one that maximizes the expected clinical outcome if followed by the patient population. We propose an improved estimator of the optimal ITR that enjoys two key properties. First, it is doubly robust, meaning that the proposed estimator is consistent if either the propensity score or the outcome model is correct. Second, it achieves the smallest variance among its class of doubly robust estimators when the propensity score model is correctly specified, regardless of the specification of the outcome model. Simulation studies show that the estimated optimal ITR obtained from our method yields better clinical outcome than its main competitors. Data from Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is analyzed as an illustrative example.
|
