|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|
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.
|Original Publication||Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions.|
|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.