A Robust Method for Estimating Optimal Treatment Regimes
Article first published online: 2 MAY 2012
© 2012, The International Biometric Society
Volume 68, Issue 4, pages 1010–1018, December 2012
How to Cite
Zhang, B., Tsiatis, A. A., Laber, E. B. and Davidian, M. (2012), A Robust Method for Estimating Optimal Treatment Regimes. Biometrics, 68: 1010–1018. doi: 10.1111/j.1541-0420.2012.01763.x
- Issue published online: 21 DEC 2012
- Article first published online: 2 MAY 2012
- Received September 2011. Revised January 2012. Accepted March 2012.
- Doubly robust estimator;
- Inverse probability weighting;
- Outcome regression;
- Personalized medicine;
- Potential outcomes;
- Propensity score
Summary 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.