Some approaches to the analysis of recurrent event data

Area of Science:

• Biostatistics
• Statistical Modeling
• Survival Analysis

Background:

• Biostatistical research has focused on Cox regression and generalized linear models (GLMs) for two decades.
• Subject-level heterogeneity and recurrent event data analysis are key challenges in these models.

Purpose of the Study:

• To review and integrate parallel developments in marginal and conditional approaches for survival analysis and GLMs.
• To clarify the relationship between frailty models, random effects GLMs, and marginal models for multivariate failure time data.

Main Methods:

• Review of methodological research in biostatistics over the last twenty years.
• Comparative analysis of 'marginal' and 'conditional' approaches in survival analysis and GLMs.
• Discussion of computational methods, including Bayesian Markov chain Monte Carlo.

Main Results:

• Frailty models are a specific instance of random effects generalizations of GLMs.
• Marginal models for multivariate failure time data align with the generalized estimating equation approach for longitudinal GLMs.

Conclusions:

• The paper provides a unified perspective on handling heterogeneity in recurrent event data within biostatistical modeling.
• Understanding the connections between different modeling frameworks is crucial for advanced statistical analysis.