title: Bivariate survival modeling: a Bayesian approach based on Copulas.
abstract: Copula models have become increasingly popular for modeling multivariate survival data. In this paper we review some of the recent work that has been appeared for copula model for bivariate survival data and propose a Bayesian modeling. Our approach is very flexible with respect to the choice of marginal distributions and, depending on the copula model employed, it is possible to have a class of variation for the dependence parameter. We compare some of the copula models using a descriptive diagnostic method and three popular Bayesian model selection criteria. Our methodology is illustrated with the Diabetic Retinopathy Study (1976).
authors.affiliation: Department of Mathematics, University of Santiago - Chile, Casilla 307 Correo 2, Santiago, Chile.
authors.forename: José S
authors.initials: JS
authors.lastname: Romeo
authors.forename: Nelson I
authors.initials: NI
authors.lastname: Tanaka
authors.forename: Antonio C
authors.initials: AC
authors.lastname: Pedroso-de-Lima
mesh_terms: D001499:Bayes Theorem; D003930:Diabetic Retinopathy; D006801:Humans; D015233:Models, Statistical; D010809:Physical Fitness; D016019:Survival Analysis