Performance of prior event rate ratio method in the presence of differential mortality or dropout
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AI 根据所提供的论文材料归类：摘要直接研究未测量混杂和差异性退出对治疗效应估计的影响。
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差异性死亡或退出下既往事件率比方法的表现
- 研究问题
- 当死亡或退出受治疗、混杂因素及既往事件不同程度影响时，既往事件率比方法的不同实现会如何影响治疗效应估计？
- 方法与思路
- 作者复现并扩展模拟，改变死亡或退出的决定因素，比较使用全部参与者的 PERR_Prev 与仅使用完成者的 PERR_Comp。
- 材料陈述的贡献
- 摘要称，在原模拟情景中，完成者估计量的偏差约为另一估计量的三分之一；部分不受既往事件影响的退出情景下未见其偏差。
- 阅读关注点
- 应核对两种估计量的定义、既往事件与退出机制的设定，以及完成者选择如何改变估计目标和模拟中的偏差比较。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实。摘要中的无偏结论依赖所考察情景，未说明其一般成立条件或实际数据表现。
原文摘要
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.