Contrasting two frameworks for ROC analysis of ordinal ratings

Area of Science:

• Biostatistics
• Medical Imaging Analysis
• Diagnostic Test Evaluation

Background:

• Receiver operating characteristic (ROC) curves are crucial for evaluating medical imaging tests.
• Ordinal regression and direct ROC modeling are popular analysis methods.
• Ordinal regression suits tests with ordinal values; direct ROC modeling suits continuous values.

Purpose of the Study:

• Compare ordinal regression and direct ROC modeling methods.
• Evaluate model formulations, parameter interpretations, and scientific question scope.
• Assess computational algorithms and data efficiencies.

Main Methods:

• Comparative analysis of two ROC modeling approaches.
• Demonstration of model equivalence for single-test evaluations.
• Examination of parameter interpretations and covariate effect relationships.

Main Results:

• Both methods fit equivalent models for single tests.
• Direct ROC modeling offers direct parameter-to-ROC curve interpretation.
• Direct ROC modeling facilitates direct comparisons between tests, even on different scales.
• Ordinal regression showed slightly higher precision in simulated data.

Conclusions:

• Direct ROC modeling provides clearer interpretation of covariate effects on ROC performance.
• Direct ROC modeling allows for direct comparisons between diverse test types.
• While slightly less data-efficient, direct ROC modeling is preferred for its interpretability and broader applicability.