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Modelling recurrent events: a tutorial for analysis in epidemiology.

Leila D A F Amorim1, Jianwen Cai2

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Summary

This study explores statistical models for recurrent events, which occur multiple times in participants. It provides guidance on analyzing this complex data using various software, improving research accuracy.

Keywords:
Recurrent eventssurvival modellingtime-to-event data

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Recurrent events, occurring multiple times per participant, are common in biomedical research.
  • Traditional analyses often focus solely on the first event, neglecting subsequent occurrences.
  • Advanced statistical methods are needed to fully capture the information from recurrent event data.

Purpose of the Study:

  • To explore and illustrate various statistical modeling techniques for recurrent time-to-event data.
  • To provide a practical tutorial for analyzing recurrent event data using common statistical software.
  • To offer recommendations for selecting appropriate modeling strategies for recurrent event data analysis.

Main Methods:

  • Conditional models for multivariate survival data (e.g., AG, PWP-TT, PWP-GT).
  • Marginal means/rates models.
  • Frailty models and multi-state models.

Main Results:

  • Demonstration of diverse modeling approaches using real-world data from bladder cancer and lower respiratory tract infection studies.
  • Comparative illustration of analysis across three widely used statistical software packages.
  • Evaluation of the applicability and performance of different recurrent event models.

Conclusions:

  • The study highlights the importance of appropriate statistical modeling for recurrent event data.
  • It provides practical insights and software guidance for researchers dealing with multiple events per subject.
  • Recommendations are offered to aid in the selection of optimal modeling strategies for recurrent event analysis.