title: Bayesian neural networks for bivariate binary data: an application to prostate cancer study.
abstract: Prostate cancer is one of the most common cancers in American men. The cancer could either be locally confined, or it could spread outside the organ. When locally confined, there are several options for treating and curing this disease. Otherwise, surgery is the only option, and in extreme cases of outside spread, it could very easily recur within a short time even after surgery and subsequent radiation therapy. Hence, it is important to know, based on pre-surgery biopsy results how likely the cancer is organ-confined or not. 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 (MP) and seminal vesicle (SV) positivity jointly. 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) in one data set and gene expression microarrays in another data set. We take a hierarchical Bayesian neural network approach to find the posterior prediction probabilities for a test and validation set, and compare these with the actual outcomes for the first data set. In case of the microarray data we use leave one out cross-validation to access the accuracy of our method. We also demonstrate the superiority of our method to the other competing methods through a simulation study. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. For the problem at hand, our Bayesian bivariate neural network procedure is shown to be superior to the classical neural network, Radford Neal's Bayesian neural network as well as bivariate logistic models to predict jointly the MP and SV in a patient in both the data sets as well as in the simulation study.
authors.affiliation: Department of Statistics, University of Florida, 103 Griffin/Floyd Hall, Gainesville, FL 32611-8545, USA. schakrab@stat.ufl.edu
authors.forename: Sounak
authors.initials: S
authors.lastname: Chakraborty
authors.forename: Malay
authors.initials: M
authors.lastname: Ghosh
authors.forename: Tapabrata
authors.initials: T
authors.lastname: Maiti
authors.forename: Ashutosh
authors.initials: A
authors.lastname: Tewari
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011471:Prostatic Neoplasms / Q000235:genetics / Q000473:pathology* / Q000628:therapy