Year: 2005
Type: article
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 modelling 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 coverage intervals and MSE of estimates of differences in the area under the curves appear to be reduced by a factor that often exceeds five. Thus, our methodology has important implications for increasing the power of analyses based on ROC data collected from an available study population.
Source: Statistics in Medicine
DOI: 10.1002/sim.2314
Authors: Timothy D. Johnson, Valen E. Johnson
Institution: University of Michigan
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Funder: National Cancer Institute
Award: G2350939763
OpenAlex ID: W2136374527