Abstract:Different methods have been employed to estimate models maximizing the area under the receiver operating characteristic curve (ROC-AUC). Once a model is developed, integrating novel biomarkers may improve its diagnostic ability. However, the discrimination improvement from adding a new biomarker is not always evident, even if the marker itself has good discriminatory power. The sign and magnitude of correlations between biomarkers may impact model performance. In this paper, we assess the effect of such correlations on the discrimination ability of predictive models. Under multivariate normality, we derive an expression for the maximum AUC as a function of the correlations between markers, illustrated graphically using surfaces. Logarithmic folded bivariate normal and Gamma simulations address skewed data cases. Additionally, AUC improvement was assessed combining 1934 blood lipid metabolites determined by liquid chromatography in 44 pancreatic cancer cases and 38 controls from the PanGenMic Study. Our results show that negative correlations consistently maximize the combined AUC, offering the greatest improvements when markers have equal predictive ability, while positive correlations yield the least favorable results. Negative correlations remain optimal for markers with differing abilities, though positive correlations show slight benefits. Simulations with skewed distributions confirm these trends, emphasizing the role of asymmetry in marker selection. Real-world analysis of serum lipid-derived metabolites for detecting pancreatic ductal adenocarcinoma (PDAC) reinforces the influence of correlations on AUC optimization. These findings suggest that the sign and magnitude of inter-biomarker correlations should be considered when incorporating new markers into predictive algorithms.
From: Sergio Sabroso-Lasa [view email]
[v1]
Mon, 1 Jun 2026 10:51:27 UTC (912 KB)
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