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Dynamic Modelling and Statistical Analysis of Event Times.

Edsel A Peña¹

¹E. Peña is Professor, Department of Statistics, University of South Carolina, Columbia, SC 29208. His e-mail address is pena@stat.sc.edu .

Statistical Science : a Review Journal of the Institute of Mathematical Statistics

|October 2, 2007

Summary

This review covers recurrent event analysis, crucial for understanding complex event data in various fields. It details advanced modeling techniques that account for multiple occurrences, covariates, and interventions, offering new insights for reliability and public health research.

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Area of Science:

Statistics
Reliability Engineering
Biostatistics
Public Health

Background:

Recurrent event data present unique analytical challenges compared to single event data.
Factors like increasing event counts, covariates, inter-event time associations, and post-event interventions complicate modeling.
Existing statistical methods may not fully capture the complexities of recurrent events.

Purpose of the Study:

To provide an overview of recent advancements in recurrent event modeling and analysis.
To describe a general class of models that simultaneously accommodate key aspects of recurrent events.
To present statistical inference methods and illustrate their application to real-world data.

Main Methods:

Review of contemporary statistical literature on recurrent event modeling.
Description of a general modeling framework for recurrent events.
Application of statistical inference methods to diverse datasets from engineering, reliability, public health, and biomedical fields.

Main Results:

A comprehensive class of models for recurrent events is presented, addressing multiple event occurrences, covariates, and interventions.
Statistical inference methods are detailed and demonstrated with practical examples.
The review highlights the applicability of these models across various scientific and engineering disciplines.

Conclusions:

The described models offer a robust framework for analyzing complex recurrent event data.
Statistical inference methods provide practical tools for researchers in multiple fields.
Open research problems in recurrent event analysis are identified, guiding future investigations.