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

TL;DR

This study evaluates how different implementations of the prior event rate ratio (PERR) method perform under various scenarios of differential mortality or dropout, highlighting the importance of operational choices in bias reduction.

Contribution

The paper extends previous simulation studies by comparing alternative PERR estimators across multiple mortality and dropout scenarios, revealing their differing biases and robustness.

Findings

PERR_Prev estimator shows bias in certain dropout scenarios.

PERR_Comp estimator reduces bias significantly in some cases.

Operationalization of PERR affects its bias and reliability.

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.