title: A semi-parametric Bayesian analysis of survival data based on Lévy-driven processes.
abstract: In the presence of covariate information, the proportional hazards model is one of the most popular models. In this paper, in a Bayesian nonparametric framework, we use a Markov (Lévy-driven) process to model the baseline hazard rate. Previous Bayesian nonparametric models have been based on neutral to the right processes, which have a number of drawbacks, such as discreteness of the cumulative hazard function. We allow the covariates to be time dependent functions and develop a full posterior analysis via substitution sampling. A detailed illustration is presented.
authors.affiliation: ITAM, México DF, México. lnieto@itam.mx
authors.forename: Luis E
authors.initials: LE
authors.lastname: Nieto-Barajas
authors.forename: Stephen G
authors.initials: SG
authors.lastname: Walker
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D016016:Proportional Hazards Models; D016019:Survival Analysis; D006113:United Kingdom