A Hierarchical Bayesian Approach to the Estimation of Monotone Hazard Rates in the Random Right Censorship Model

Here we study hierarchical Bayesian estimation of a monotone hazard rate for both complete and randomly right censored data. We propose two methods of computation: Monte-Carlo importance sampling and Laplace approximation techniques. These methods are computationally simple and easily implemented on complex hazard functions. They are compared in simulation studies with uncensored and censored data and the methodology is illustrated on two interesting data sets.