A Robust Method for Estimating Optimal Treatment Regimes

TLDR

A treatment regime is a rule that assigns a treatment based on patient characteristics, and for a single decision it can be derived from a regression model of expected outcome conditional on treatment and covariates, selecting the treatment that maximizes expected outcome. The study aims to identify the optimal treatment regime that maximizes average population outcome, even when the underlying regression model may be misspecified, by selecting the regime that optimizes an estimator of overall mean outcome. We use a doubly robust augmented inverse probability weighted estimator to account for confounding and improve precision when estimating the optimal regime. The method’s performance is demonstrated by simulations and a breast cancer trial, though assignment based on a misspecified regression model can be unreliable.

Abstract

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.