Robust Medical Test Evaluation Using Flexible Bayesian Semiparametric Regression Models
Joint Authors
Baron, Andre T.
Johnson, Wesley O.
Branscum, Adam J.
Source
Epidemiology Research International
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-11
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Branscum, Adam J.& Johnson, Wesley O.& Baron, Andre T.. 2013. Robust Medical Test Evaluation Using Flexible Bayesian Semiparametric Regression Models. Epidemiology Research International،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-448160
Modern Language Association (MLA)
Branscum, Adam J.…[et al.]. Robust Medical Test Evaluation Using Flexible Bayesian Semiparametric Regression Models. Epidemiology Research International No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-448160
American Medical Association (AMA)
Branscum, Adam J.& Johnson, Wesley O.& Baron, Andre T.. Robust Medical Test Evaluation Using Flexible Bayesian Semiparametric Regression Models. Epidemiology Research International. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-448160
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-448160