Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions

Baqun Zhang, Anastasios A. Tsiatis, Eric B. Laber and Marie Davidian

Biometrika, 2013, vol. 100, issue 3, 681-694

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. Copyright 2013, Oxford University Press.

Date: 2013
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