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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.

SUBMITTER: Zhang B

PROVIDER: S-EPMC3843953 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions.

Zhang Baqun B Tsiatis Anastasios A AA Laber Eric B EB Davidian Marie M

Biometrika 20130101 3

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]

PMID: 24302771

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