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.

翻译：关于病人治疗战略的保健政策决定既需要考虑治疗的有效性,也需要考虑费用问题。在有效性方面优化治疗规则可能会导致令人望而生畏的昂贵战略;另一方面,在成本方面优化可能会导致病人的不良结果。我们建议采取分两步走的办法,确定一种最具成本效益和可解释的动态治疗制度。首先,我们开发一种综合的问答和政策研究办法,在预期治疗费用的限制下估计一个以清单为基础的最佳制度。第二,我们提议采用迭接程序,从一套符合不同费用限制的候选制度中选择一种最符合成本效益的制度。我们的方法可以在共同遇到的挑战,包括时间变化和相互关联的结果时,对最佳制度作出估计。我们通过模拟研究,说明估计的最佳治疗制度的有效性,并根据灵活的模型方法审查运作特点。