Comput Methods Programs Biomed - Bayesian bivariate generalized Lindley model for survival data with a cure fraction.

Tópicos

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Resumo

The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie-Gumbel-Morgenstern (FGM), Clayton and Gumbel-Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.

Resumo Limpo

cure fraction model wide use analyz surviv data proport individu suscept event interest articl introduc bivari model surviv data cure fraction base threeparamet general lindley distribut joint distribut surviv time obtain use copula function consid three type copula function model farliegumbelmorgenstern fgm clayton gumbelbarnett copula model implement bayesian framework paramet estim base markov chain mont carlo mcmc techniqu illustr util model consid applic real data set relat invas cervic cancer studi

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