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An analytic expression for the binormal partial area under the ROC curve.
Stephen L Hillis1, Charles E Metz
1Departments of Radiology and Biostatistics, University of Iowa, 3170 Medical Laboratories, 200 Hawkins Drive, Iowa City, IA 52242-1077, USA. steve-hillis@uiowa.edu
Academic Radiology
|November 6, 2012
Summary
Researchers have derived analytic expressions for the partial area under the ROC curve (pAUC) in diagnostic studies. This simplifies the computation of pAUC for binormal ROC curves, improving diagnostic accuracy analysis.
Area of Science:
- Medical Imaging Analysis
- Diagnostic Test Evaluation
- Statistical Modeling in Medicine
Background:
- The partial area under the receiver operating characteristic (ROC) curve (pAUC) is a key metric for evaluating diagnostic tests.
- Current methods for calculating pAUC in latent binormal models rely on approximations or numerical integration.
- An analytic expression for pAUC has been lacking, hindering efficient computation.
Purpose of the Study:
- To derive and present analytic expressions for the two forms of partial area under the ROC curve (pAUC).
- To provide a more direct and efficient method for computing pAUC for binormal ROC curves.
Main Methods:
- Derivation of analytic expressions for two distinct types of pAUC.
- Mathematical proofs supporting the derived expressions.
- Application of the analytic expressions using a comparative example of MRI techniques for thoracic aortic dissection detection.
Main Results:
- Analytic expressions for both forms of pAUC have been successfully derived.
- Demonstrated the utility of pAUC as an outcome measure in multireader, multicase analyses.
- The use of pAUC in analysis led to more statistically significant findings.
Conclusions:
- The availability of analytic expressions simplifies the computation of pAUC for binormal ROC curves.
- This advancement facilitates more robust and efficient evaluation of diagnostic test performance.
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