Biomarker assessment in ROC curve analysis using the length of the curve as an index of diagnostic accuracy: the binormal model framework

Alba M. Franco-Pereira (), Christos T. Nakas and M. Carmen Pardo
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Alba M. Franco-Pereira: Complutense University of Madrid
Christos T. Nakas: University of Thessaly
M. Carmen Pardo: Complutense University of Madrid

AStA Advances in Statistical Analysis, 2020, vol. 104, issue 4, No 4, 625-647

Abstract: Abstract In receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) is undoubtedly the most widely used index of diagnostic accuracy for the assessment of the utility of a biomarker or for the comparison of competing biomarkers. Along with the AUC, the maximum of the Youden index, J, is often used both as an index of diagnostic accuracy and as a tool useful for the estimation of an optimal cutoff point that can be used for diagnostic purposes based on the biomarker under consideration. In this work, we study the utility of the length of the binormal model-based ROC curve (LoC) as an index of diagnostic accuracy for biomarker evaluation. Estimation procedures for LoC, described in this article, are based either on normality assumptions or on the same assumptions after a Box–Cox transformation to normality. Two simulation studies are considered. In the first, the estimation procedures for LoC are compared in terms of bias and root mean squared error, while in the second one, the performance of LoC is compared with approaches based on AUC and J, both for the case of the assessment of a single biomarker and for the comparison of two biomarkers, in a parametric framework. We provide an interpretation for the proposed index and illustrate with an application on biomarkers from a colorectal cancer study.

Keywords: Area under the ROC curve (AUC); Length of the ROC curve (LoC); Binormal ROC curve; Maximum of the Youden index (J); Diagnostic likelihood ratio (DLR) (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10182-020-00371-8

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