Novel tests for evaluating two ROC curves under paired samples.

Area of Science:

• Biostatistics
• Medical Diagnostics
• Health Informatics

Background:

• Disease prevention relies heavily on accurate diagnostic tests.
• Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) are standard metrics for evaluating diagnostic accuracy.
• Comparing two diagnostic tests is crucial, but existing methods struggle when ROC curves intersect.

Purpose of the Study:

• To develop a novel statistical test for comparing the accuracy of two diagnostic tests.
• To address limitations of current methods when dealing with crossing ROC curves.
• To incorporate potential correlations between paired samples in the comparison.

Main Methods:

• Proposed a new statistical test designed for paired sample comparisons of diagnostic accuracy.
• The test explicitly considers the correlation between paired observations.
• Feasibility and performance were evaluated through simulation studies.

Main Results:

• The developed test demonstrates feasibility in simulations.
• The method accounts for paired sample correlations, a key advantage over existing approaches.
• Provides a more reliable assessment when ROC curves cross.

Conclusions:

• The proposed test offers an improved method for comparing diagnostic accuracy, particularly in scenarios with crossing ROC curves.
• This advancement can lead to more reliable diagnostic test selection for disease prevention.
• Further validation and application in real-world datasets are warranted.