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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Constructing "proper" ROCs from ordinal response data using weighted power functions.

Douglas Mossman¹, Hongying Peng¹

¹University of Cincinnati College of Medicine, Cincinnati, OH (DM, HP).

Medical Decision Making : an International Journal of the Society for Medical Decision Making

|September 14, 2013

Summary

The weighted power function (WPF) model provides a superior alternative to the conventional binormal model for Receiver Operating Characteristic (ROC) analysis. This new model generates proper ROC curves, offering more accurate diagnostic system evaluation.

Keywords:

ROC analysis proper ROC receiver operating characteristic weighted power function

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Area of Science:

Biostatistics
Medical Informatics
Diagnostic Accuracy

Background:

Receiver Operating Characteristic (ROC) analysis is standard for diagnostic accuracy.
Conventional binormal models often yield improper ROC curves with undesirable features like hooks.
These flaws can lead to misinterpretation of diagnostic system performance.

Purpose of the Study:

To introduce and evaluate a novel 2-parameter, weighted power function (WPF) model for ROC analysis.
To address the limitations of the traditional binormal model.
To ensure the generation of proper ROC curves with consistent properties.

Main Methods:

A computer simulation study was conducted.
Results from the WPF model were compared against the binormal model.
Model performance was assessed based on bias and accuracy.

Main Results:

The WPF model generated ROC curves that were less biased and closer to true values compared to the binormal model.
The inherent design of the WPF model as a proper ROC contributed to its superior performance.
The WPF model demonstrated better accuracy in diagnostic system evaluation.

Conclusions:

The WPF model offers a more accurate and reliable method for ROC analysis.
It overcomes the limitations and potential misinterpretations associated with the binormal model.
The WPF model is a simple, effective, and broadly applicable tool for evaluating diagnostic systems.