New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.

TitleNew Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.
Publication TypeJournal Article
Year of Publication2015
AuthorsZhao, Ying-Qi, Donglin Zeng, Eric B. Laber, and Michael R. Kosorok
JournalJ Am Stat Assoc
Date Published2015

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 fundamentally different than regression-based methods, for example -learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with -learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation.

Alternate JournalJ Am Stat Assoc
Original PublicationNew statistical learning methods for estimating optimal dynamic treatment regimes.
PubMed ID26236062
PubMed Central IDPMC4517946
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States