Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates under two treatments within a defined target population over a specified followup window. Estimation with observational data is challenging because, while membership in the target population is defined in terms of eligibility criteria, treatment is rarely observed exactly at the time of eligibility. Ad-hoc solutions to this timing misalignment can induce bias by incorrectly attributing prior event counts and person-time to treatment. Even if eligibility and treatment are aligned, a terminal event process (e.g. death) often stops the recurrent event process of interest. In practice, both processes can be censored so that events are not observed over the entire followup window. Our approach addresses misalignment by casting it as a time-varying treatment problem: some patients are on treatment at eligibility while others are off treatment but may switch to treatment at a specified time - if they survive long enough. We define and identify an average causal effect estimand under right-censoring. Estimation is done using a g-computation procedure with a joint semiparametric Bayesian model for the death and recurrent event processes. We apply the method to contrast hospitalization rates among patients with different opioid treatments using Medicare insurance claims data.

翻译：在生物医学统计学中，对复发事件发生率的观察性研究十分常见。其总体目标是在特定随访窗口内，估计目标人群中两种治疗下事件发生率的差异。使用观察性数据进行估计具有挑战性，因为尽管目标人群的成员资格是根据资格标准定义的，但治疗很少在恰好符合资格的时间点被观察到。针对这种时序错位的临时解决方案，可能通过错误地将先前的事件计数和人时归因于治疗而引入偏倚。即使资格与治疗在时间上对齐，一个终止事件过程（例如死亡）也常常会中断所关注的复发事件过程。在实践中，这两个过程都可能被删失，导致在整个随访窗口内无法观察到所有事件。我们的方法通过将时序错位视为一个时变治疗问题来解决：一些患者在符合资格时即接受治疗，而另一些患者则未接受治疗，但可能在特定时间点（如果他们存活足够长）转为治疗。我们定义并在右删失条件下识别了一个平均因果效应估计量。估计是使用g-计算程序，并结合针对死亡和复发事件过程的联合半参数贝叶斯模型来完成的。我们应用该方法，利用医疗保险理赔数据，对比了接受不同阿片类药物治疗患者的住院率。