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Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
<span title="2013-05-30">2013</span>
<i title="Oxford University Press (OUP)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6oeltljhrzfq7brjdtp2wqehpu" style="color: black;">Biometrika</a>
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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
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/biomet/ast014">doi:10.1093/biomet/ast014</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24302771">pmid:24302771</a>
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... 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.
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