Optimal treatment rules can improve health outcomes on average by assigning a treatment associated with the most desirable outcome to each individual. Due to an unknown data generation mechanism, it is appealing to use flexible models to estimate these rules. However, such models often lead to complex and uninterpretable rules. In this article, we introduce an approach aimed at estimating optimal treatment rules that have higher accuracy, higher value, and lower loss from the same simple model family. We use a flexible model to estimate the optimal treatment rules and a simple model to derive interpretable treatment rules. We provide an extensible definition of interpretability and present a method that - given a class of simple models - can be used to select a preferred model. We conduct a simulation study to evaluate the performance of our approach compared to treatment rules obtained by fitting the same simple model directly to observed data. The results show that our approach has lower average loss, higher average outcome, and greater power in identifying individuals who can benefit from the treatment. We apply our approach to derive treatment rules of adjuvant chemotherapy in colon cancer patients using cancer registry data. The results show that our approach has the potential to improve treatment decisions.

翻译：最优治疗规则可通过为每位患者分配与最理想结果相关的治疗方案，从而在平均水平上改善健康结局。由于数据生成机制未知，采用灵活模型估算这些规则具有吸引力，但此类模型往往导致复杂且难以解释的规则。本文提出一种方法，旨在从同一简单模型族中估计出具有更高精度、更高价值和更低损失的最优治疗规则。我们利用灵活模型估计最优治疗规则，并通过简单模型推导可解释的治疗规则。我们提供了可扩展的可解释性定义，并提出了一种方法——给定一类简单模型后——可用于选择优选模型。我们开展模拟研究，评估本方法相较于直接对观测数据拟合相同简单模型所得治疗规则的性能。结果表明，本方法具有更低的平均损失、更高的平均结局，以及在识别能从治疗中获益的个体方面更强的统计效力。我们应用本方法，利用癌症登记数据推导结肠癌患者辅助化疗的治疗规则。结果表明，本方法具有改善治疗决策的潜力。