Dynamic Modeling and Statistical Analysis of Event Times

TL;DR

This review discusses recent advances in modeling recurrent event times across various fields, emphasizing the complexity of such models and their statistical inference methods, with applications and open research challenges.

Contribution

It introduces a comprehensive class of models for recurrent events that incorporate multiple complex factors and discusses inference methods and applications.

Findings

A new general class of recurrent event models is described.

Statistical inference methods are developed and illustrated.

Open research problems in recurrent event analysis are identified.

Abstract

This review article provides an overview of recent work in the modeling and analysis of recurrent events arising in engineering, reliability, public health, biomedicine and other areas. Recurrent event modeling possesses unique facets making it different and more difficult to handle than single event settings. For instance, the impact of an increasing number of event occurrences needs to be taken into account, the effects of covariates should be considered, potential association among the interevent times within a unit cannot be ignored, and the effects of performed interventions after each event occurrence need to be factored in. A recent general class of models for recurrent events which simultaneously accommodates these aspects is described. Statistical inference methods for this class of models are presented and illustrated through applications to real data sets. Some existing open research problems are described.