Department of Biostatistics, University at Buffalo, Buffalo, NY, USA.
Stat Methods Med Res. 2019 May;28(5):1328-1346. doi: 10.1177/0962280218755810. Epub 2018 Feb 2.
In the field of diagnostic studies for tree or umbrella ordering, under which the marker measurement for one class is lower or higher than those for the rest unordered classes, there exist a few diagnostic measures such as the naive AUC ( NAUC), the umbrella volume ( UV), and the recently proposed TAUC, i.e. area under a ROC curve for tree or umbrella ordering (TROC). However, an important characteristic about tree or umbrella ordering has been neglected. This paper mainly focuses on promoting the use of the integrated false negative rate under tree ordering ( ITFNR) as an additional diagnostic measure besides TAUC, and proposing the idea of using ( TAUC, ITFNR) instead of TAUC to evaluate the diagnostic accuracy of a biomarker under tree or umbrella ordering. Parametric and non-parametric approaches for constructing joint confidence region of ( TAUC, ITFNR) are proposed. Simulation studies under a variety of settings are carried out to assess and compare the performance of these methods. In the end, a published microarray data set is analyzed.
在树状或伞状排序的诊断研究领域中，一类标志物的测量值低于或高于其他未排序类的测量值，存在一些诊断指标，如 naive AUC（NAUC）、伞形体积（UV）和最近提出的 TAUC，即树状或伞状排序的 ROC 曲线下面积（TROC）。然而，树状或伞状排序的一个重要特征被忽视了。本文主要关注的是推广使用树状排序下的综合假阴性率（ITFNR）作为 TAUC 的附加诊断指标，并提出使用（TAUC，ITFNR）代替 TAUC 来评估标志物在树状或伞状排序下的诊断准确性的想法。提出了用于构建（TAUC，ITFNR）联合置信区间的参数和非参数方法。在各种设置下进行了模拟研究，以评估和比较这些方法的性能。最后，分析了一个已发表的微阵列数据集。
