title: Mixture and non-mixture cure fraction models based on the generalized modified Weibull distribution with an application to gastric cancer data.
abstract: The cure fraction models are usually used to model lifetime time data with long-term survivors. In the present article, we introduce a Bayesian analysis of the four-parameter generalized modified Weibull (GMW) distribution in presence of cure fraction, censored data and covariates. In order to include the proportion of "cured" patients, mixture and non-mixture formulation models are considered. To demonstrate the ability of using this model in the analysis of real data, we consider an application to data from patients with gastric adenocarcinoma. Inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods.
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.forename: Jorge A
authors.initials: JA
authors.lastname: Achcar
authors.forename: Alexandre A A
authors.initials: AA
authors.lastname: Jácome
authors.forename: José S
authors.initials: JS
authors.lastname: Santos
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008962:Models, Theoretical; D013274:Stomach Neoplasms / Q000473:pathology*
keywords: Bayesian analysis; Cure fraction model; Gastric cancer; Generalized modified Weibull distribution; Survival analysis