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An alternative parameterization for the binormal ROC curve, with applications to sizing and simulation studies.

Stephen L Hillis1

  • 1The University of Iowa.

Proceedings of Spie--The International Society for Optical Engineering
|July 15, 2024
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
AUC variance estimationarea under the ROC curve (AUC)binormal ROC curvebinormal a and b parametersmean-tosigma ratiomulti-reader multi-case (MRMC)simulation study

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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.