Paper Information

Clinicians have increasingly turned to F-scores to gauge the accuracy of diagnostic tests. However, the dependency of F-scores on the prevalence of the underlying illness poses challenges, especially when prevalence varies across regions or populations, potentially leading to misdiagnoses. To address this issue, this article presents novel post-test diagnostic precision metrics for continuous tests or biomarkers. These metrics are based on the collective areas under the F-score curves across all conceivable prevalence values. Unlike traditional measures, the proposed metrics remain constant regardless of disease prevalence, enabling fair comparisons of different diagnostic tests and biomarkers' abilities in rule-in, rule-out, and overall accuracy. The article also explores the relationship between the proposed metrics and other diagnostic accuracy measures. Numerical illustrations and a real-world breast cancer dataset exemplify the application of the proposed metrics.