Small area estimation of receiver operating characteristic curves for ordinal data under stochastic ordering. (3rd February 2020)

• Record Type: Journal Article
• Authors: Jang, Eun Jin; Nandram, Balgobin; Ko, Yousun; Kim, Dal Ho
• Is Part Of: Statistics in medicine. Volume 39:Number 10(2020)
• Page Start: 1514
• Page End: 1528
• Publication Date: 2020-02-03
• Subjects: area under the curve -- Bayesian hierarchical model -- diagnostic test -- grid method -- proper ROC curve -- stochastic order; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8493
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : There has been a recent increase in the diagnosis of diseases through radiographic images such as x‐rays, magnetic resonance imaging, and computed tomography. The outcome of a radiological diagnostic test is often in the form of discrete ordinal data, and we usually summarize the performance of the diagnostic test using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The ROC curve will be concave and called proper when the outcomes of the diagnostic test in the actually positive subjects are higher than in the actually negative subjects. The diagnostic test for disease detection is clinically useful when a ROC curve is proper. In this study, we develop a hierarchical Bayesian model to estimate the proper ROC curve and AUC using stochastic ordering in several domains when the outcome of the diagnostic test is discrete ordinal data and compare it with the model without stochastic ordering. The model without stochastic ordering can estimate the improper ROC curve with a nonconcave shape or a hook when the true ROC curve of the population is a proper ROC curve. Therefore, the model with stochastic ordering is preferable over the model without stochastic ordering to estimate the proper ROC curve with clinical usefulness for ordinal data.