title: Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions.
abstract: A dynamic treatment regime is a list of sequential decision rules for assigning treatment based on a patient's history. Q- and A-learning are two main approaches for estimating the optimal regime, i.e., that yielding the most beneficial outcome in the patient population, using data from a clinical trial or observational study. Q-learning requires postulated regression models for the outcome, while A-learning involves models for that part of the outcome regression representing treatment contrasts and for treatment assignment. We propose an alternative to Q- and A-learning that maximizes a doubly robust augmented inverse probability weighted estimator for population mean outcome over a restricted class of regimes. Simulations demonstrate the method's performance and robustness to model misspecification, which is a key concern.
authors.affiliation: Department of Preventive Medicine, 680 N. Lakeshore Drive, Suite 1400 Northwestern University, Chicago, Illinois, 60611 U.S.A.
authors.forename: Baqun
authors.initials: B
authors.lastname: Zhang
authors.forename: Anastasios A
authors.initials: AA
authors.lastname: Tsiatis
authors.forename: Eric B
authors.initials: EB
authors.lastname: Laber
authors.forename: Marie
authors.initials: M
authors.lastname: Davidian
keywords: A-learning; Double robustness; Outcome regression; Propensity score; Q-learning