Current Methods for Recurrent Events Data With Dependent Termination A Bayesian Perspective

There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid future users in understanding the implications of modeling assumptions and modeling properties, we review the state-...

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Veröffentlicht in:Journal of the American Statistical Association Jg. 103; H. 482; S. 866 - 878
Hauptverfasser: Sinha, Debajyoti, Maiti, Tapabrata, Ibrahim, Joseph G, Ouyang, Bichun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Alexandria, VA Taylor & Francis 01.06.2008
American Statistical Association
Taylor & Francis Ltd
Schlagworte:
Algorithms
Applications
Bayesian analysis
Censorship
Clinical research
Clinical trials
Data
Estimation
Exact sciences and technology
Frailty
General topics
Gibbs sampler
Gibbs sampling
Global analysis, analysis on manifolds
Graft rejection
Health care costs
Inference
Logical givens
M-site model
Markov analysis
Mathematics
Medical sciences
Mixed Poisson process
Modeling
Multivariate analysis
Musical intervals
Novels
Parametric models
Probability and statistics
Property
Recurrent
Recurrent events
Research methods
Review Article
Sciences and techniques of general use
Semiparametric Bayes
Statistical analysis
Statistical methods
Statistics
Stochastic models
Stochastic processes
Termination
Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds
Usefulness
Biometrics
Bayes estimation
Stochastic model
Monte Carlo method
Data analysis
Identifiability
Semiparametric Bayes
Statistical distribution
Mixed Poisson process
Statistical association
Statistical estimation
Multivariate analysis
Stochastic method
Gibbs sampling
M-site model
Markov chain
Statistical method
Medical science
Numerical analysis
Risk rate
Clinical trial
Frailty
Approximation theory
Application
ISSN:0162-1459, 1537-274X
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Beschreibung
Zusammenfassung:There has been a recent surge of interest in modeling and methods for analyzing recurrent events data with risk of termination dependent on the history of the recurrent events. To aid future users in understanding the implications of modeling assumptions and modeling properties, we review the state-of-the-art statistical methods and present novel theoretical properties, identifiability results, and practical consequences of key modeling assumptions of several fully specified stochastic models. After introducing stochastic models with 2 noninformative termination process, we focus on a class of models that allows both negative and positive association between the risk of termination and the rate of recurrent events through a frailty variable. We also discuss the relationship, as well as the major differences between these models in terms of their motivations and physical interpretations. We discuss associated Bayesian methods based on Markov chain Monte Carlo tools, and novel model diagnostic tools to perform inference based on fully specified models. We demonstrate the usefulness of the current methodology through an analysis of a data set from a clinical trial. Finally, we explore possible future extensions and limitations of the methodology.
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ISSN:0162-1459
1537-274X
DOI:10.1198/016214508000000201