Constant Hazard Function Models with a Change Point: A Bayesian Analysis Using Markov Chain Monte Carlo Methods

Metropolis algorithms along with Gibbs steps are proposed to perform a Bayesian analysis for change-point constant hazard function models considering different prior densities for the parameters and censored survival data. We also present some generalizations for the comparison of two treatments. The methodology is illustrated with some examples.