存在竞争性终点事件时复发事件的最优治疗策略估计：一种工具变量差分法
Optimal Treatment Policy Estimation for Recurrent Events with a Competing Terminal Event: An Instrumented Difference-in-Differences Approach
AI总结：
针对存在竞争性终点事件的复发事件，提出基于工具变量差分法（iDID）的框架来估计最优治疗策略，通过多重稳健估计量解决未测量混杂问题，并避免因增加死亡率而减少不良事件的策略。
摘要：
学习慢性疾病的可重复和可推广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一个自然的起点，但其使用常受未测量混杂因素的限制。我们通过提出一种基于工具变量差分法（iDID）的新框架来解决这一问题，以估计受终止事件影响的复发事件结果的最优策略。iDID设计在此场景中特别有用，因为它利用政策诱导的治疗变异，同时允许不同人群之间存在持续的未测量差异，其假设比传统IV或DID方法所需的假设对行政健康数据更合理。我们方法的一个关键特征是明确解决了避免那些通过增加死亡率而简单减少复发不良事件的策略这一基本挑战。我们推导了两种不同的逆概率加权识别方法，并开发了一种多重稳健估计量，只要多个子集的干扰模型中的任何一个被正确设定，该估计量就能达到一致性。通过大样本理论，我们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下优于现有方法的性能。最后，我们将该框架应用于一个全国性的Medicare数据集，以优化一线2型糖尿病策略，具体目标是减少疾病相关住院，同时考虑生存情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
