A Bayesian Hierarchical Approach to Multirater Correlated ROC Analysis

Tim Johnson and Valen Johnson
Additional contact information
Tim Johnson: University of Michigan Biostatistics
Valen Johnson: University of Michigan School of Public Health

No 1027, The University of Michigan Department of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: In a common ROC study design, several readers are asked to rate diagnostics of the same cases processed under different modalities. We describe a Bayesian hierarchical model that facilitates the analysis of this study design by explicitly modeling the three sources of variation inherent to it. In so doing, we achieve substantial reductions in the posterior uncertainty associated with estimates of the differences in areas under the estimated ROC curves and corresponding reductions in the mean squared error (MSE) of these estimates. Based on simulation studies, both the widths of confidence intervals and MSE of estimates of differences in the area under the curves appear to be reduced by a factor that often exceeds two. Thus, our methodology has important implications for increasing the power of analyses based on ROC data collected from an available study population.

Keywords: ROC curve; Receiver operating characteristics; Bayesian analysis; Hierarchical model; Ordinal data; Latent variables (search for similar items in EconPapers)
Date: 2004-09-20
Note: oai:bepress.com:umichbiostat-1027
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.bepress.com/cgi/viewcontent.cgi?article=1027&context=umichbiostat (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bep:mchbio:1027

Access Statistics for this paper

More papers in The University of Michigan Department of Biostatistics Working Paper Series from Berkeley Electronic Press
Bibliographic data for series maintained by Christopher F. Baum ().