New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.

ABSTRACT: Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamen

SUBMITTER: Zhao YQ 

PROVIDER: S-EPMC4517946 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature