title: Bayesian bivariate generalized Lindley model for survival data with a cure fraction.
abstract: 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.
authors.affiliation: Department of Social Medicine, University of São Paulo (USP), Ribeirão Preto School of Medicine, Brazil. Electronic address: edson@fmrp.usp.br.
authors.forename: Edson Z
authors.initials: EZ
authors.lastname: Martinez
authors.affiliation: Department of Social Medicine, University of São Paulo (USP), Ribeirão Preto School of Medicine, Brazil.
authors.forename: Jorge A
authors.initials: JA
authors.lastname: Achcar
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D009026:Mortality; D010363:Pattern Recognition, Automated / Q000379:methods*; D012307:Risk Factors; D016019:Survival Analysis; D002583:Uterine Cervical Neoplasms / Q000401:mortality*
keywords: Bayesian analysis; Copula function; Cure fraction model; Lindley distribution; Survival analysis