Bayesian model for the receiver operating characteristic curve using the skew normal distribution†
Eun Jin Jang1 · Dal Ho Kim2
1Department of Information Statistics, Andong National University
2Department of Statistics, Kyungpook National University
2Department of Statistics, Kyungpook National University
Correspondence to: 1Associate professor, Department of Information Statistics, Andong National University, Andong 36729, Korea.
2Corresponding author: Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: dalkim@knu.ac.kr
†This work was supported by a Research Grant of Andong National University.
2Corresponding author: Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: dalkim@knu.ac.kr
†This work was supported by a Research Grant of Andong National University.
Received December 29, 2020; Revised January 8, 2021; Accepted January 15, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- The results of diagnostic tests for disease diagnosis are measured by continuous or ordinal data. The performance of diagnostic tests usually be summarized using the receiver operating characteristic (ROC) curve and the area under the curve. The diagnostic tests are clinically useful when the test results in the diseased group are higher than those in the non-diseased group, in which case the ROC curve is called a proper ROC curve. In this study, we consider the skew normal distribution for the latent variables of ordinal data and the stochastic ordering methods to estimate the proper ROC curve in Bayesian model, and apply them to the real data.
- Keywords : Bayesian model, diagnostic test, ordinal data, receiver operating characteristic curve, skew normal distribution.
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