Bayesian Semiparametric Inference for the Accelerated Failure Time (AFT) Model Using Hierarchical Mixture Modeling with N-IG Priors

• Activity Number: 458
• Type: Topic Contributed
• Date/Time: Wednesday, August 9, 2006 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Alessandra Guglielmi*+ and Raffaele Argiento and Antonio Pievatolo and Fabrizio Ruggeri
• Companies: Politecnico di Milano and Università Commerciale Luigi Bocconi/CNR-IMATI and CNR-IMATI and CNR-IMATI
• Keywords: AFT regression models ; Bayesian semiparametrics ; mixture models ; MCMC algorithms

Abstract

We will pursue a Bayesian semiparametric approach for an AFT regression model, usually considered in survival analysis, when the baseline survival distribution is a mixture of parametric densities on the positive reals with a nonparametric mixing measure. A popular choice for the mixing measure is a Dirichlet process, yielding an MDP model for the error. Here, as an alternative to the Dirichlet process, the mixing measure is equal to an N-IG prior, built from normalized inverse-Gaussian finite dimensional distributions, as recently proposed in the literature. A comparison of the models will be carried out. MCMC techniques will be used to estimate the predictive distribution of the survival time, along with the posterior distribution of the regression parameters. The efficiency of computational methods also will be compared, using both real and simulated data.