Performance of prior event rate ratio method in the presence of differential mortality or dropout

Abstract

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.