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Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions. (English) Zbl 1284.62508
Summary: A dynamic treatment regime is a list of sequential decision rules for assigning treatments 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 assignments. 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.

MSC:
62L10 Sequential statistical analysis
62C99 Statistical decision theory
62P10 Applications of statistics to biology and medical sciences; meta analysis
68T05 Learning and adaptive systems in artificial intelligence
62G35 Nonparametric robustness
65C60 Computational problems in statistics (MSC2010)
Software:
qLearn
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