A Bayesian Semiparametric AFT Model for Interval-Censored Data

Abstract

We model the baseline distribution in the accelerated failure-time (AFT) model as a mixture of Dirichlet processes for interval-censored data. This mixture is distinct from Dirichlet process mixtures, and can be viewed as a simple extension of existing parametric models, which we believe is an advantage in the practical modeling of data. We introduce a novel MCMC scheme for the purpose of making posterior inferences for the AFT regression model and illustrate our methods with several real examples.

Key Words:

• Accelerated failure time
• Mixture of dirichlet processes
• Regression