title: On Bayesian inference for proportional hazards models using noninformative priors.
abstract: In this article, we investigate the properties of the posterior distribution under the uniform improper prior for two commonly used proportional hazards models; the Weibull regression model and the extreme value regression model. We allow the observations to be right censored. We obtain sufficient conditions for the existence of the posterior moment generating function of the regression coefficients. A dataset involving a lung cancer clinical trial and a simulation are presented to illustrate our results.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health and Dana Farber Cancer Institute, 44 Binney St., Boston, MA 02115, USA.
authors.forename: S W
authors.initials: SW
authors.lastname: Kim
authors.forename: J G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000188:drug therapy / Q000401:mortality; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012044:Regression Analysis; D016019:Survival Analysis