Volume : III, Issue : VI, June - 2014

MODELING OF PARAMETRIC BAYESIAN CURE RATE SURVIVAL FOR PULMONARY TUBERCULOSIS DATA ANALYSIS

N. Sundaram, P. Venkatesan

Abstract :

The cure fraction refers to the proportion of patients who are cured of disease constituting long-term survivors. The study of cure fraction gives a useful measure of disease control and provides better predictions of long term survival rates to researchers and policy makers. In this article we study the parametric Bayesian cure rate model for rightcensored data for population with a surviving fraction relating to two cases namely with and without frailty. We assume normal prior for covariates and Gamma prior for shape parameter. The estimates of the parameters are obtained using the Markov chain Monte Carlo (MCMC) technique. A real dataset from a pulmonary tuberculosis clinical trial is used in this research paper. Distributions including Exponential, Exponentiated Exponential, Weibull, Log-Logistic, Gamma and Log-Normal have been considered and a comparison of the results is presented.

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Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

N.Sundaram, P.Venkatesan Modeling of Parametric Bayesian Cure Rate Survival for Pulmonary Tuberculosis Data Analysis International Journal of Scientific Research, Vol.III, Issue.VI June 2014

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