Bayesian model selection: analysis of a survival model with a surviving fraction

Abstract

We describe a methodology for model comparison in a Bayesian framework as applied to survival with a surviving fraction. This is illustrated using a case study of a randomized and controlled clinical trial investigating time until recurrence of depression. Posterior distributions are simulated using Metropolis-within-Gibbs Markov chain methods. Models reflecting the effects of covariates on the log odds of being in the surviving fraction, the log of the hazard rate, as well as both and neither are compared. Bayes factors for comparing the models are obtained by using the bridge sampling method of calculating normalizing constants.