Dynamic treatment regimes (DTRs) are sequences of functions that formalize the process of precision medicine. DTRs take as input patient information and output treatment recommendations. A major focus of the DTR literature has been on the estimation of optimal DTRs, the sequences of decision rules that result in the best outcome in expectation, across the complete population were they to be applied. While there is a rich literature on optimal DTR estimation, to date there has been minimal consideration of the impacts of nonadherence on these estimation procedures. Nonadherence refers to any process through that an individual's prescribed treatment does not match their true treatment. We explore the impacts of nonadherence and demonstrate that generally, when nonadherence is ignored, suboptimal regimes will be estimated. In light of these findings we propose a method for estimating optimal DTRs in the presence of nonadherence. The resulting estimators are consistent and asymptotically normal, with a double robustness property. Using simulations we demonstrate the reliability of these results, and illustrate comparable performance between the proposed estimation procedure adjusting for the impacts of nonadherence and estimators that are computed on data without nonadherence.

翻译：动态治疗方案（DTRs）是一系列函数序列，用于形式化精准医学的过程。DTRs以患者信息为输入，输出治疗建议。DTR文献的一个主要焦点在于估计最优DTRs——若应用于全人群，能带来期望最佳结果的决策规则序列。尽管关于最优DTR估计已有丰富文献，但迄今对非依从性如何影响这些估计程序仍鲜有探讨。非依从性指个体被处方治疗与其实际治疗不匹配的任意过程。我们探究了非依从性的影响，并证明若忽略非依从性，通常会导致次优方案被估计。基于这些发现，我们提出了一种在非依从性存在下估计最优DTRs的方法。所得估计量具有一致性和渐近正态性，并具备双稳健性质。通过仿真实验，我们验证了这些结果的可靠性，并表明调整非依从性影响的估计程序与基于无非依从性数据计算的估计量之间性能相当。