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Computational and Mathematical Methods in Medicine
Volume 2017 (2017), Article ID 3762651, 14 pages
https://doi.org/10.1155/2017/3762651
Research Article

Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach

School of Medicine, Department of Biostatistics, Cukurova University, Saricam, Adana, Turkey

Correspondence should be addressed to Ilker Unal; rt.ude.uc@nurekli

Received 7 January 2017; Revised 5 April 2017; Accepted 7 May 2017; Published 31 May 2017

Academic Editor: Hiro Yoshida

Copyright © 2017 Ilker Unal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The analysis results in two gains: diagnostic accuracy of the biomarker and the optimal cut-point value. There are many methods proposed in the literature to obtain the optimal cut-point value. In this study, a new approach, alternative to these methods, is proposed. The proposed approach is based on the value of the area under the ROC curve. This method defines the optimal cut-point value as the value whose sensitivity and specificity are the closest to the value of the area under the ROC curve and the absolute value of the difference between the sensitivity and specificity values is minimum. This approach is very practical. In this study, the results of the proposed method are compared with those of the standard approaches, by using simulated data with different distribution and homogeneity conditions as well as a real data. According to the simulation results, the use of the proposed method is advised for finding the true cut-point.