Statistical Methods for Recurrent Event Data Analysis

Recurrent event data occur when study subjects may experience the same type of event multiple times over a follow-up period. Analysing such data requires methods that accommodate within-subject correlation, time-varying risks and various forms of censoring. Broadly speaking, two main frameworks prevail: marginal models, which treat recurrent events as independent conditionally on covariates, and conditional or frailty models, which introduce latent variables to capture unobserved heterogeneity. Extensions to these frameworks include joint models that link recurrent events with a terminal outcome, multi-state models that allow transitions between states, and accelerated failure time models that characterise the temporal dynamics of event occurrence. Choice of time scale—gap time (resetting after each event) versus total time (measuring from study start)—and the handling of informative drop-out are further considerations. Semi-parametric approaches, such as Cox-type proportional hazards models, offer flexibility in hazard specification, while fully parametric or piecewise parametric models can improve efficiency when model assumptions hold. Computational advances have enabled the application of these methods to large-scale and high-dimensional settings, enhancing their relevance to clinical trials, chronic disease studies, genomics, engineering reliability and public health surveillance. Practical deployment often relies on specialised software for simulation, estimation and graphical diagnostics, facilitating sample size planning and ensuring robust inference in the analysis of complex event processes.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Statistical Methods for Recurrent Event Data Analysis publication trend

The graph below shows the total number of articles in statistical methods for recurrent event data analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Recurrent event data: Data in which the same type of event may occur repeatedly for each subject during follow-up.

Cox proportional hazards model: A semi-parametric regression model that relates covariates to the hazard rate, assuming constant hazard ratios over time.

Frailty model: A survival-analysis approach introducing subject-specific random effects to capture unobserved heterogeneity in event risk.

Accelerated failure time model: A parametric model that describes how covariates accelerate or decelerate the time until an event occurs.

Zero-inflated model: A regression framework that accounts for excess non-occurrence of events by incorporating a point mass at zero.

Saddlepoint approximation: A technique for improving the accuracy of distributional approximations of test statistics, especially in high-dimensional or sparse data settings.

References

1. Regression Modeling for Recurrent Events Possibly with an Informative Terminal Event Using R Package reReg. Journal of Statistical Software (2023).
2. A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases. BMC Medical Informatics and Decision Making (2023).
3. Fast and accurate recurrent event analysis for genome‐wide association studies. Genetic Epidemiology (2023).