Optimal dynamic treatment regime estimation in the presence of nonadherence.

Abstract

Dynamic treatment regimes (DTRs) are sequences of functions that formalize the process of precision medicine. DTRs take as input patient information and output treatment recommendations. A major focus of the DTR literature has been on the estimation of optimal DTRs, the sequences of decision rules that result in the best outcome in expectation, across the complete population if they were to be applied. While there is a rich literature on optimal DTR estimation, to date, there has been minimal consideration of the impacts of nonadherence on these estimation procedures. Nonadherence refers to any process through which an individual's prescribed treatment does not match their true treatment. We explore the impacts of nonadherence and demonstrate that, generally, when nonadherence is ignored, suboptimal regimes will be estimated. In light of these findings, we propose a method for estimating optimal DTRs in the presence of nonadherence. The resulting estimators are consistent and asymptotically normal, with a double robustness property. Using simulations, we demonstrate the reliability of these results, and illustrate comparable performance between the proposed estimation procedure adjusting for the impacts of nonadherence and estimators that are computed on data without nonadherence.