A Bayesian approach for the analysis of panel-count data with dependent termination.
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.
Sinha
Debajyoti
D
Department of Biometry, Medical University of South Carolina, Charleston, South Carolina 29425, USA. sinhad@musc.edu
Maiti
Tapabrata
T
D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D009010:Monte Carlo Method; D013269:Stochastic Processes; D001749:Urinary Bladder Neoplasms