Purpose: Prior event rate ratio (PERR) method was proposed to control for measured or unmeasured confounders in real-world evaluation of effectiveness and safety of medical treatments using electronic medical records data. A widely cited simulation study showed that PERR estimate of treatment effect was biased in the presence of differential morality/dropout. However, the study only considered one specific PERR estimator of treatment effect and one specific scenario of differential mortality/dropout. To enhance understanding of the method, we replicated and extended the simulation to consider an alternative PERR estimator and multiple scenarios. Methods: Simulation studies were performed with varying rate of mortality/dropout, including the scenario in the previous study in which mortality/dropout was simultaneously influenced by treatment, confounder and prior event and scenarios that differed in the determinants of mortality/dropout. In addition to the PERR estimator used in the previous study (PERR_Prev) that involved data form both completers and non-completers, we also evaluated an alternative PERR estimator (PERR_Comp) that used data only from completers. Results: The bias of PERR_Prev in the previously considered mortality/dropout scenario was replicated. Bias of PERR_Comp was only about one-third in magnitude as compared to that of PERR_Prev in this scenario. Furthermore, PERR_Prev did but PERR_Comp did not give biased estimates of treatment effect in scenarios that mortality/dropout was influenced by treatment or confounder but not prior event. Conclusion: The PERR is better seen as a methodological framework within which there is more than one way to operationalize the estimation. Its performance depends on the specific operationalization. PERR_Comp provides unbiased estimates unless mortality/dropout is affected by prior event.

翻译：目的：既往事件率比（PERR）方法被提出用于在使用电子病历数据进行真实世界医疗效果和安全性评估时，控制已测量或未测量的混杂因素。一项被广泛引用的模拟研究表明，在存在差异死亡率/失访的情况下，PERR对治疗效应的估计存在偏倚。然而，该研究仅考虑了一种特定的PERR治疗效应估计量以及一种特定的差异死亡率/失访情景。为了加深对该方法的理解，我们复制并扩展了该模拟研究，以考虑另一种PERR估计量以及多种情景。方法：模拟研究在变化的死亡率/失访率下进行，包括先前研究中死亡率/失访同时受治疗、混杂因素和既往事件影响的场景，以及死亡率/失访决定因素不同的其他场景。除了先前研究中使用的同时包含完成者和未完成者数据的PERR估计量（PERR_Prev）外，我们还评估了另一种仅使用完成者数据的PERR估计量（PERR_Comp）。结果：在先前考虑的死亡率/失访情景中，PERR_Prev的偏倚得到了复现。在此情景下，PERR_Comp的偏倚幅度仅为PERR_Prev的大约三分之一。此外，在死亡率/失访受治疗或混杂因素影响但不受既往事件影响的场景中，PERR_Prev确实给出了有偏的治疗效应估计，而PERR_Comp则没有。结论：PERR最好被视为一个方法学框架，其中存在不止一种实施估计的方式。其性能取决于具体的实施方式。除非死亡率/失访受既往事件影响，否则PERR_Comp能提供无偏估计。