Tree-based Ensemble Methods For Individualized Treatment Rules

Abstract

There is a growing interest in statistical methods for the personalized medicine or precision medicine, especially for deriving optimal individualized treatment rules (ITRs). An ITR recommends a patient to a treatment based on the patient’s characteristics. The common parametric methods for deriving optimal ITR, which model the clinical endpoint as a function of the patient’s characteristics in the first step, can have suboptimal performance when the conditional mean model is misspecified. Recent methodology development has cast the problem of deriving optimal ITR under a weighted classification framework. Under this weighted classification framework, we develop a weighted random forests (W-RF) algorithm that derives an optimal ITR nonparametrically. In addition, with the W-RF algorithm, we propose the variable importance measures for quantifying relative relevance of the patient’s characteristics to treatment selection, and the out-of-bag estimator for the population aver- age outcome under the estimated optimal ITR. Our proposed methods are evaluated through intensive simulation studies. We apply our methods to data from Clinical Antipsychotic Trials of Intervention Effectiveness Alzheimer’s Disease Study (CATIE-AD) as an illustration.