Dynamic Treatment Regimes with Replicated Observations Available for Error-Prone Covariates: A Q-Learning Approach

Dynamic treatment regimes (DTRs) have received increasing interests in recent years. DTRs are sequences of treatment decision rules tailored to patient-level information. The main goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that yields the best expected clinical outcome. Q-learning has been regarded as one of the most popular regression-based methods for estimating the optimal DTR. However, it has been rarely studied in an error-prone setting, where patient information is contaminated with measurement error. In this article, we shed light on the effect of covariate measurement error on Q-learning and propose an effective method to correct the error in Q-learning. Simulation studies are conducted to assess the performance of the proposed correction method in Q-learning. We illustrate the use of the proposed method in an application to the Sequenced Treatment Alternatives to Relieve Depression data.