Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions

B. Zhang, A. A. Tsiatis, E. B. Laber, M. Davidian
2013 Biometrika  
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
more » ... 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.
doi:10.1093/biomet/ast014 pmid:24302771 pmcid:PMC3843953 fatcat:waa54cpndncj5jegplyvs7th7q