A new diagnostic accuracy measure and cut-point selection criterion. (December 2017)

• Record Type: Journal Article
• Authors: Dong, Tuochuan; Attwood, Kristopher; Hutson, Alan; Liu, Song; Tian, Lili
• Is Part Of: Statistical methods in medical research. Volume 26:Number 6(2017)
• Page Start: 2832
• Page End: 2852
• Publication Date: 2017-12
• Subjects: Maximum absolute determinant -- optimal cut-points -- volume for the parallelotope -- Alzheimer's Disease; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280215611631
• Languages: English
• ISSNs: 0962-2802

Abstract

Most diagnostic accuracy measures and criteria for selecting optimal cut-points are only applicable to diseases with binary or three stages. Currently, there exist two diagnostic measures for diseases with general k stages: the hypervolume under the manifold and the generalized Youden index. While hypervolume under the manifold cannot be used for cut-points selection, generalized Youden index is only defined upon correct classification rates. This paper proposes a new measure named maximum absolute determinant for diseases with k stages (k ≥ 2 ). This comprehensive new measure utilizes all the available classification information and serves as a cut-points selection criterion as well. Both the geometric and probabilistic interpretations for the new measure are examined. Power and simulation studies are carried out to investigate its performance as a measure of diagnostic accuracy as well as cut-points selection criterion. A real data set from Alzheimer's Disease Neuroimaging Initiative is analyzed using the proposed maximum absolute determinant.