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A Bayesian Approach for the Analysis of Panel‐Count Data with Dependent Termination
Author(s) -
Sinha Debajyoti,
Maiti Tapabrata
Publication year - 2004
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2004.00140.x
Subject(s) - markov chain monte carlo , bayesian probability , computer science , count data , variable order bayesian network , data set , random effects model , bayesian inference , econometrics , statistics , artificial intelligence , mathematics , medicine , meta analysis , poisson distribution
Summary. We consider modeling and Bayesian analysis for panel‐count data when the termination time for each subject may depend on its history of the recurrent events. We propose a fully specified semiparametric model for the joint distribution of the recurrent events and the termination time. For this model, we provide a natural motivation, derive several novel properties, and develop a Bayesian analysis based on a Markov chain Monte Carlo algorithm. Comparisons are made to other existing models and methods for panel‐count data. We demonstrate the usefulness of our new models and methodologies through the reanalysis of a data set from a clinical trial.