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Current methods for recurrent events data with dependent termination: A Bayesian perspective
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DOI：10.1198/016214508000000201.png)
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Abstract
En 中文
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.
Keywords:
frailty
Gibbs sampling
mixed Poisson process
M-site model
semiparametric Bayes
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