Jang Eun Jin, Nandram Balgobin, Ko Yousun, Kim Dal Ho
Department of Information Statistics, Andong National University, Andong, South Korea.
Department of Mathematical Sciences, Worcester Polytechnic Institute, Worcester, Massachusetts.
Stat Med. 2020 May 15;39(10):1514-1528. doi: 10.1002/sim.8493. Epub 2020 Feb 3.
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.
近年来，通过X射线、磁共振成像和计算机断层扫描等放射影像进行疾病诊断的情况有所增加。放射诊断测试的结果通常是离散有序数据的形式，我们通常使用接收者操作特征（ROC）曲线和曲线下面积（AUC）来总结诊断测试的性能。当实际阳性受试者的诊断测试结果高于实际阴性受试者时，ROC曲线将是凹形的且被称为合适的。当ROC曲线合适时，用于疾病检测的诊断测试在临床上是有用的。在本研究中，当诊断测试的结果是离散有序数据时，我们开发了一种分层贝叶斯模型，以使用多个领域中的随机排序来估计合适的ROC曲线和AUC，并将其与没有随机排序的模型进行比较。当总体的真实ROC曲线是合适的ROC曲线时，没有随机排序的模型可以估计出形状为非凹形或有弯钩的不合适的ROC曲线。因此，对于有序数据，具有随机排序的模型比没有随机排序的模型更适合估计具有临床实用性的合适的ROC曲线。
