A robust method for estimating optimal treatment regimes.

TitleA robust method for estimating optimal treatment regimes.
Publication TypeJournal Article
Year of Publication2012
AuthorsZhang, Baqun, Anastasios A. Tsiatis, Eric B. Laber, and Marie Davidian
Date Published2012 Dec
KeywordsBreast Neoplasms, Clinical Trials as Topic, Computer Simulation, Decision Support Systems, Clinical, Female, Humans, Models, Statistical, Outcome Assessment, Health Care, Prevalence, Regression Analysis, Treatment Outcome

A treatment regime is a rule that assigns a treatment, among a set of possible treatments, to a patient as a function of his/her observed characteristics, hence "personalizing" treatment to the patient. The goal is to identify the optimal treatment 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 treatment and covariates, where, for a given set of covariates, the optimal treatment is the one that yields the most favorable expected outcome. However, treatment assignment via such a regime is suspect if the regression model is incorrectly specified. Recognizing that, even if misspecified, such a regression model defines a class of regimes, we instead consider finding the optimal regime within such a 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.

Alternate JournalBiometrics
Original PublicationA robust method for estimating optimal treatment regimes.
PubMed ID22550953
PubMed Central IDPMC3556998
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA051962 / CA / NCI NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R37 AI031789 / AI / NIAID NIH HHS / United States