Table of Contents
Epidemiology Research International
Volume 2013, Article ID 131232, 8 pages
Research Article

Robust Medical Test Evaluation Using Flexible Bayesian Semiparametric Regression Models

1Biostatistics Program, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA
2Department of Statistics, University of California, Irvine, CA 92697, USA
3Tumor Biology Investment Group, Inc., Richmond, KY 40475, USA

Received 6 August 2013; Accepted 31 October 2013

Academic Editor: Leo J. Schouten

Copyright © 2013 Adam J. Branscum et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The application of Bayesian methods is increasing in modern epidemiology. Although parametric Bayesian analysis has penetrated the population health sciences, flexible nonparametric Bayesian methods have received less attention. A goal in nonparametric Bayesian analysis is to estimate unknown functions (e.g., density or distribution functions) rather than scalar parameters (e.g., means or proportions). For instance, ROC curves are obtained from the distribution functions corresponding to continuous biomarker data taken from healthy and diseased populations. Standard parametric approaches to Bayesian analysis involve distributions with a small number of parameters, where the prior specification is relatively straight forward. In the nonparametric Bayesian case, the prior is placed on an infinite dimensional space of all distributions, which requires special methods. A popular approach to nonparametric Bayesian analysis that involves Polya tree prior distributions is described. We provide example code to illustrate how models that contain Polya tree priors can be fit using SAS software. The methods are used to evaluate the covariate-specific accuracy of the biomarker, soluble epidermal growth factor receptor, for discerning lung cancer cases from controls using a flexible ROC regression modeling framework. The application highlights the usefulness of flexible models over a standard parametric method for estimating ROC curves.