How to Make Better Diagnostic Tests: Comparing Optimal Youden and Euclidean Cut-Points

• Activity Number: 414 - Risk Modeling and Regression Techniques
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): William Barbeau* and Fares Qeadan
• Companies: University of Utah and University of Utah
• Keywords: Diagnostic test; ROC; sensitivity; specificity; optimal cut-point; Youden Index

Abstract

Diagnostic tests, based on continuous biomarker measurements, have become essential tools in medicine. Designing diagnostic tests based on continuous biomarkers is challenging because the biomarker’s distribution in the population with the disease and population without the disease are rarely completely separated. Overlap in these two distributions means the test will always result in misclassification. The Youden index and Euclidean index (AKA the point closest to (0,1)) are two popular methods for choosing optimal cut-points to maximize sensitivity and specificity of diagnostic tests. Despite both of these methods being described and deployed in practice, there is little guidance on which method to use and at which situation. Through mathematical derivations, we show in the binormal case that the Euclidean index, relative to the Youden index, yields optimal thresholds with lower absolute differences between sensitivity and specificity, especially when the difference in biomarker variances is large. If developers of diagnostic tests aim to maximize sensitivity and specificity for normally distributed data, then the Euclidean index is the preferred optimal cut-point method.