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

Franco-Pereira, Alba M.; Nakas, Christos T.; Pardo, M. Carmen (2020). Biomarker assessment in ROC curve analysis using the length of the curve as an index of diagnostic accuracy: the binormal model framework. AStA Advances in statistical analysis, 104(4), pp. 625-647. Springer 10.1007/s10182-020-00371-8

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry

UniBE Contributor:

Nakas, Christos T.

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1863-8171

Publisher:

Springer

Language:

English

Submitter:

Karin Balmer

Date Deposited:

09 Nov 2020 15:25

Last Modified:

12 Nov 2020 01:34

Publisher DOI:

10.1007/s10182-020-00371-8

BORIS DOI:

10.7892/boris.147803

URI:

https://boris.unibe.ch/id/eprint/147803

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