The refinement of repeated exact time-to-event modeling: methodological refinements and implementation in NONMEM® —part I

Abstract

Background Parametric time-to-event (TTE) modeling is widely used in clinical trials to evaluate treatment response, patient prognosis, and drug-related adverse events (AEs). As follow-up assessments increasingly capture recurrent AEs, there is a growing need for analytical approaches that extend beyond traditional single-event frameworks. However, commonly used TTE models often assume independence between recurrent events, limiting their ability to represent inter-event dependence observed in real-world clinical data. Methods We mathematically examined and reconstructed the existing NONMEM® (version 7.5) control code used for parametric repeated-event analyses and evaluated the statistical foundations of commonly applied parametric and nonparametric approaches, including Kaplan–Meier–based methods. Based on these analyses, we identified methodological gaps and proposed an improved probabilistic framework that more rigorously represents recurrent event dynamics while allowing for multiple dependent event occurrences. The proposed and existing methods were compared through simulation studies under diverse event-generation scenarios, and practical guidance for model selection was developed. Results Three approaches were evaluated: two conventional parametric methods (methods 1, 2) and a newly proposed method (method 3). Overall, when the hazard function remained constant, all three methods produced similar results; however, clear differences in performance emerged depending on hazard dynamics and event density. When the hazard decreased gradually and events were relatively evenly distributed, method 2 demonstrated the closest alignment with nonparametric estimates, whereas when the hazard declined rapidly and events were concentrated in the early phase, method 3 showed a relatively better fit. In addition, because nonparametric estimators inherently involve uncertainty, discrepancies between parametric and nonparametric approaches do not necessarily indicate model misspecification, underscoring the need for careful interpretation in light of data characteristics and underlying hazard structures. Conclusion These findings highlight the importance of selecting analytical methods according to data characteristics and suggest that complementary use of both widely used and newly proposed methods can improve the accuracy of recurrent event modeling. The proposed guidelines are expected to facilitate more systematic and reliable interpretation of recurrent AE data frequently encountered in clinical trials and real-world clinical settings, ultimately contributing to more robust evaluations of drug efficacy and safety as well as optimization of dosing strategies.