title: Bayesian Transformation Models for Multivariate Survival Data.
abstract: In this paper we propose a general class of gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported.
authors.affiliation: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo.
authors.forename: Mário
authors.initials: M
authors.lastname: DE Castro
authors.affiliation: Department of Statistics, University of Connecticut.
authors.forename: Ming-Hui
authors.initials: MH
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, University of North Carolina.
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
authors.affiliation: Division of Biostatistics, Medical College of Wisconsin.
authors.forename: John P
authors.initials: JP
authors.lastname: Klein
keywords: Gibbs sampler; cure rate; gamma frailty; piecewise exponential model; proportional hazards model; proportional odds model