We describe a model comparison problem approached from a a Bayesian perspective. This is illustrated with a case study of a randomized and controlled clinical trial investigating recurrence of depression. The time until recurrence is modeled as a survival model with a surviving fraction. Posterior distributions are simulated using Metropolis-within-Gibbs Markov chain methods. Sixteen versions of linear combinations of two covariates in the log odds of being in the surviving fraction and the log of the hazard rate are modeled. Bayes factors for comparing the models are obtained by using the bridge sampling method of calculating normalizing constants.