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Published on: September 16, 2022
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An alternative parameterization for the binormal ROC curve, with applications to sizing and simulation studies.
1The University of Iowa.
Summary
This study introduces a new way to describe Receiver Operating Characteristic (ROC) curves using the mean-to-sigma ratio and area under the curve (AUC). This simplifies understanding ROC curve shape and size for diagnostic tests.
Area of Science:
- Medical Diagnostics
- Biostatistics
- Medical Imaging Analysis
Background:
- Conventional Receiver Operating Characteristic (ROC) curve parameters rely on underlying normal distributions for diseased and nondiseased cases.
- Understanding ROC curve shape and size requires complex transformations of these conventional parameters.
Purpose of the Study:
- To propose an alternative parameterization for ROC curves that directly describes their shape and size.
- To introduce parameters that are easily interpretable by users.
Main Methods:
- Proposed two parameters: mean-to-sigma ratio and area under the ROC curve (AUC).
- The mean-to-sigma ratio quantifies ROC curve improperness.
- AUC quantifies diagnostic test discrimination ability.
Main Results:
- The new parameterization simplifies the interpretation of ROC curve shape and size.
- Facilitates diagnostic study sizing with conjectured variance components.
- Simplifies the selection of binormal parameters for simulation studies.
Conclusions:
- The proposed parameterization offers a more intuitive understanding of ROC curves.
- This approach enhances the efficiency of diagnostic study design and simulation.
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