Abstract #301935

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JSM 2003 Abstract #301935
Activity Number: 254
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 12:00 PM to 1:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301935
Title: Bayesian Neural Networks for Bivariate Binary Data: An Application to Prostate Cancer Study
Author(s): Sounak Chakraborty*+ and Malay Ghosh and Tapabrata Maiti
Companies: University of Florida and University of Florida and Iowa State University
Address: 1700 SW 16th Ct. Apt. G21, Gainesville, FL, 32608-1577,
Keywords: neural network ; hierarchical Bayesian model ; prostate cancer ; Markov chain Monte Carlo ; bivariate binary data
Abstract:

The paper considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ-confined prostate cancer. In particular, we find such probabilities for margin positivity and SV positivity. The available training set consists of bivariate binary outcomes indicating the presence or absence of the two. In addition, we have certain covariates such as prostate specific antigen (PSA), Gleason Score and the indicator for the cancer to be unilateral or bilateral (i.e., spread on one or both sides). We take a hierarchical Bayesian neural network approach to find the posterior prediction probabilities for a test set, and compare these with the actual outcomes. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. For the problem at hand, the bivariate neural network procedure is shown to be superior to the univariate hierarchical Bayesian neural network applied separately to predict Margin and SV positivity.

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