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Health policy decisions regarding patient treatment strategies requireconsideration of both treatment effectiveness and cost. Optimizing treatmentrules with respect to effectiveness may result in prohibitively expensivestrategies; on the other hand, optimizing with respect to costs may result inpoor patient outcomes. We propose a two-step approach for identifying anoptimally cost-effective and interpretable dynamic treatment regime. First, wedevelop a combined Q-learning and policy-search approach to estimate an optimallist-based regime under a constraint on expected treatment costs. Second, wepropose an iterative procedure to select an optimally cost-effective regimefrom a set of candidate regimes corresponding to different cost constraints.Our approach can estimate optimal regimes in the presence of time-varyingconfounding, censoring, and correlated outcomes. Through simulation studies, weillustrate the validity of estimated treatment regimes and examine operatingcharacteristics under flexible modeling approaches. We also apply ourmethodology to evaluate optimally cost-effective treatment strategies forassigning adjuvant therapies to endometrial cancer patients.

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