Accommodating misclassification effects on optimizing dynamic treatment regimes with Q-learning.

Research on dynamic treatment regimes has enticed extensive interest. Many methods have been proposed in the literature, which, however, are vulnerable to the presence of misclassification in covariates. In particular, although Q-learning has received considerable attention, its applicability to data with misclassified covariates is unclear. In this article, we investigate how ignoring misclassification in binary covariates can impact the determination of optimal decision rules in randomized treatment settings, and demonstrate its deleterious effects on Q-learning through empirical studies. We present two correction methods to address misclassification effects on Q-learning. Numerical studies reveal that misclassification in covariates induces non-negligible estimation bias and that the correction methods successfully ameliorate bias in parameter estimation.