Bayesian analysis of right censored survival time data

We analyzed cancer data using Fully Bayesian inference approach based on Markov Chain Monte Carlo (MCMC) simulation technique which allows the estimation of very complex and realistic models. The results show that sex and age are significant risk factors for dying from some selected cancers. The risk of dying from these cancers is observed to progressively increase as age of patients increases. It is also observed that in order to allow for nonlinearity due to metrical covariate age, the semiparametric P-splines model is better than the model that categorizes age into various age groups.