Department of Biostatistics, University at Buffalo, Buffalo, New York, USA.
Stat Med. 2021 Jul 20;40(16):3695-3723. doi: 10.1002/sim.8993. Epub 2021 Apr 27.
This article considers a setting in diagnostic studies (or biomarker study) which involves a healthy class and a diseased class and the latter consists of several subclasses. The problem of interest is to evaluate the accuracy of a biomarker (or a diagnostic test) measured on a continuous scale correctly identifying healthy subjects from diseased subjects without requiring specification of an ordering in terms of marker values for subclasses relative to each other within the diseased class. Such setting is quite common in practice and it falls in the framework of tree ordering or umbrella ordering. This article explores several parametric and nonparametric approaches for estimating confidence intervals of sensitivity of single biomarker and difference between sensitivities of two correlated biomarkers under tree ordering at a given specificity. The performances of all the methods are evaluated and compared by a comprehensive simulation study. A published microarray data set is analyzed using the proposed methods.
本文考虑了一种诊断研究（或生物标志物研究）的情况，其中涉及健康组和疾病组，后者由几个亚组组成。感兴趣的问题是评估在连续尺度上测量的生物标志物（或诊断测试）的准确性，以便正确识别健康受试者和患病受试者，而无需根据患病组内各亚组的标记值相对于彼此的顺序进行指定。这种设置在实践中很常见，它属于树序或伞序的框架。本文探讨了几种参数和非参数方法，用于在给定特异性下估计单生物标志物敏感性的置信区间和两个相关生物标志物敏感性之间的差异，这些方法在树序下。通过全面的模拟研究评估和比较了所有方法的性能。使用提出的方法分析了一个已发表的微阵列数据集。
