Title | Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Zhang, Baqun, Anastasios A. Tsiatis, Eric B. Laber, and Marie Davidian |
Journal | Biometrika |
Volume | 100 |
Issue | 3 |
Date Published | 2013 |
ISSN | 0006-3444 |
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. |
DOI | 10.1093/biomet/ast014 |
Alternate Journal | Biometrika |
Original Publication | Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions. |
PubMed ID | 24302771 |
PubMed Central ID | PMC3843953 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 CA085848 / CA / NCI NIH HHS / United States R37 AI031789 / AI / NIAID NIH HHS / United States |
Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions.
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