ROC analysis with multiple classes and multiple tests: methodology and its application in microarray studies
ROC analysis with multiple classes and multiple tests: methodology and its application in microarray studies
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DOI：
10.1093/biostatistics/kxm050
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发表时间：
2008-07-01
期刊：
影响因子：
2.1
通讯作者：
Fine, Jason P.
中科院分区：
文献类型：
--
作者：
Li, Jialiang;Fine, Jason P.
单一诊断试验对二元结果的准确性可以用受试者工作特征曲线下的面积来概括。表面下体积和流形下的超体积已被提出作为多类诊断的扩展（Scurfield, 1996,1998）。但是，由于缺乏简单的推理程序，限制了这些措施的实际效用。部分困难在于，即使通过一次测试，计算这些数量也可能不那么简单。用于生成ROC曲面的决策规则需要类概率评估，这是测试不提供的。我们开发了一种基于概率估计的方法，例如，多项逻辑回归。提出了自举推理来解释估计概率的可变性，并在模拟中表现良好。ROC测量值与正确分类率进行比较，正确分类率在很大程度上取决于类别患病率。用微阵列数据进行肿瘤分类的一个例子表明，这一特性可能导致本质上不同的分析。与之前的分析相比，基于roc的分析显著降低了模型的复杂性。
The accuracy of a single diagnostic test for binary outcome can be summarized by the area under the receiver operating characteristic (ROC) curve. Volume under the surface and hypervolume under the manifold have been proposed as extensions for multiple class diagnosis (Scurfield, 1996, 1998). However, the lack of simple inferential procedures for such measures has limited their practical utility. Part of the difficulty is that calculating such quantities may not be straightforward, even with a single test. The decision rule used to generate the ROC surface requires class probability assessments, which are not provided by the tests. We develop a method based on estimating the probabilities via some procedure, for example, multinomial logistic regression. Bootstrap inferences are proposed to account for variability in estimating the probabilities and perform well in simulations. The ROC measures are compared to the correct classification rate, which depends heavily on class prevalences. An example of tumor classification with microarray data demonstrates that this property may lead to substantially different analyses. The ROC-based analysis yields notable decreases in model complexity over previous analyses.