Identifying optimally cost-effective dynamic treatment regimes with a Q-learning approach

Health policy decisions regarding patient treatment strategies require consideration of both treatment effectiveness and cost. Optimizing treatment rules with respect to effectiveness may result in prohibitively expensive strategies; on the other hand, optimizing with respect to costs may result in poor patient outcomes. We propose a two-step approach for identifying an optimally cost-effective and interpretable dynamic treatment regime. First, we develop a combined Q-learning and policy-search approach to estimate an optimal list-based regime under a constraint on expected treatment costs. Second, we propose an iterative procedure to select an optimally cost-effective regime from a set of candidate regimes corresponding to different cost constraints. Our approach can estimate optimal regimes in the presence of commonly encountered challenges including time-varying confounding and correlated outcomes. Through simulation studies, we illustrate the validity of estimated optimal treatment regimes and examine operating characteristics under flexible modeling approaches. Using data from an observational cancer database, we apply our methodology to evaluate optimally cost-effective treatment strategies for assigning adjuvant radiation and chemotherapy to endometrial cancer patients.