A Robust Method for Estimating Optimal Treatment Regimes
Thursday, March 28, 2013 - 4:30pm
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A treatment regime is a rule that assigns a treatment, among a set of possible treatment options, to a patient as a function of his/her individual characteristics, hence \personalizing" treatment to the patient. A goal is to identify the optimal treatment regime; that is, the regime that, if followed by the entire population of patients, would lead to the best outcome on average. Given data from a clinical trial or observational study, for a single treatment decision, the optimal regime can be found by assuming a regression model for the expected outcome conditional on observed treatment and covariates, where, for a given set of covariates, the optimal treatment is the one that yields the most favorable expected outcome. However, clearly, treatment assignment via such a regime is suspect if this regression model is incorrectly specified. Even if misspecified, such a regression model defines a class of regimes; moreover, for reasons of cost, interpretability, or feasibility of implementation, investigators may wish to focus on a class of regimes having a certain form. Accordingly, rather than focusing on the optimal regime, we consider finding the optimal regime within a specified class by finding the regime that optimizes an estimator of overall population mean outcome. To take into account possible confounding in an observational study and to increase precision, we use a doubly robust augmented inverse probability weighted estimator for this purpose. Simulations and application to data from a breast cancer clinical trial demonstrate the performance of the method. Joint work with: Baqun Zhang, Northwestern University; Anastasios A. Tsiatis and Eric. B. Laber, North Carolina State University This is the annual UGA-Clemson Joint Seminar held at Clemson this year.

M-101 Martin Hall, Clemson University