An Application to Prostate Cancer Study Prostate cancer is one of the most common cancers in American men.
Management depends on the staging of prostate cancer.
Only cancers that are confined to organs of origin are potentially curable. This paper considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ confined prostate cancer. The Bayesian procedure is implemented by the Markov Chain Monte Carlo numerical integration technique. For the problem at hand, the neural network method is shown to be superior to one based on a hierarchical Bayesian logistic regression model.