Survival regression models with dependent Bayesian nonparametric priors

We will present a novel Bayesian nonparametric model for regression in survival analysis. The model builds on the neutral to the right model of Doksum (1974) and on the Cox proportional hazards model of Kim and Lee (2003). The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates whilst allowing non-proportionality. Properties of the model and inference schemes will be discussed. The method will be illustrated using simulated and real data. (Joint work with Fabrizio Leisen, University of Nottingham, U.K.)