Learning reproducible and generalizable optimal treatment policies for chronic diseases requires large, representative populations with long-term follow-up. Administrative health data provide a natural starting point, but their use is often limited by unmeasured confounding. We address this by proposing a novel framework based on Instrumented Difference-in-Differences (iDID) to estimate optimal policies for recurrent event outcomes subject to a terminating event. The iDID design is particularly useful in this setting because it leverages policy-induced treatment variation while allowing for persistent unmeasured differences across populations, relying on assumptions that are more plausible for administrative health data than those required by conventional IV or DID approaches. A key feature of our approach is that it explicitly addresses the fundamental challenge of avoiding policies that trivially reduce recurrent adverse events by increasing mortality. We derive two distinct Inverse Probability Weighted identifications and develop a multiply robust estimator that achieves consistency if any one of several subsets of nuisance models is correctly specified. We establish the estimator's consistency and asymptotic normality through large-sample theory and demonstrate its superior finite-sample performance over existing methods via simulation. Finally, we apply this framework to a national Medicare dataset to optimize first-line Type 2 Diabetes strategies, specifically targeting the minimization of disease-related hospitalizations while accounting for survival.

翻译：学习可重复且可推广的慢性病最优治疗策略需要具有长期随访的大规模代表性人群。行政健康数据提供了天然起点，但其使用常受未测量混杂因素限制。我们通过提出基于工具变量差分差分法（iDID）的新框架来解决此问题，以估计受终止事件影响的复发事件结局的最优策略。iDID设计在此场景中特别有用，因为它利用政策驱动的治疗变异，同时允许跨人群存在持续的未测量差异，其依赖的假设比传统IV或DID方法对行政健康数据更合理。我们方法的关键特征在于明确解决了避免通过增加死亡率来简单减少复发不良事件的策略这一根本挑战。我们推导出两种逆概率加权识别方法，并开发了多重稳健估计量，该估计量在多个子集的干扰模型中有任一正确设定时即可实现一致性。通过大样本理论建立了估计量的一致性和渐近正态性，并通过模拟证明了其在有限样本性能上优于现有方法。最后，我们将该框架应用于全国医疗保险数据集以优化2型糖尿病一线治疗策略，具体目标是疾病相关住院的最小化，同时考虑生存情况。