A Bayesian approach for the analysis of panel-count data with dependent termination.

Debajyoti Sinha; Tapabrata Maiti

doi:10.1111/j.0006-341x.2004.00140.x

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A Bayesian approach for the analysis of panel-count data with dependent termination.

Debajyoti Sinha·Tapabrata Maiti

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Abstract

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.

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Algorithms Bayes Theorem Biometry Data Interpretation, Statistical Humans Likelihood Functions Markov Chains Monte Carlo Method Stochastic Processes Urinary Bladder Neoplasms Comparative Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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Debajyoti Sinha, Tapabrata Maiti. 2004. A Bayesian approach for the analysis of panel-count data with dependent termination.. https://doi.org/10.1111/j.0006-341x.2004.00140.x

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