ABSTRACT Prior event rate ratio (PERR) is a method shown to perform well in mitigating confounding and is gaining popularity in real‐world evidence research. However, it depends on several model assumptions. We propose an analytic strategy to correct biases arising from violation of two model assumptions, namely, population homogeneity and event‐independent treatment. We propose a reformulation of PERR estimation by embedding a treatment‐by‐period interaction term in the Andersen‐Gill model for recurrent event data, which is robust to bias arising from unobserved heterogeneity. Based on this model, we propose a set of methods to examine the presence of event‐dependent treatment and to correct the resultant bias. We evaluate the proposed methods by simulation and apply it to a de‐identified dataset on palliative care and emergency department visits in patients with advanced cancer. Simulation results showed that the proposed method could mitigate the two sources of bias in PERR. In the palliative care study, analysis by the Cox model showed that patients who had started receiving palliative care had higher incidence of emergency department visits than their match controls (hazard ratio 3.31; 95% confidence interval 2.78–3.94). Using PERR without the proposed bias control strategy indicated a 19% reduction of the incidence (0.81; 0.64–1.02). However, there was evidence of event‐dependent treatment. The proposed correction method showed no effect of palliative care on ED visits (1.00; 0.79–1.26). In conclusion, the proposed analytic strategy can control two sources of biases in the PERR approach. It enriches the armamentarium for real‐world evidence research.
Ma et al. (2026) studied this question.