Suppr超能文献

二元数据的多读者多病例方差分析。

Multireader multicase variance analysis for binary data.

作者信息

Gallas Brandon D, Pennello Gene A, Myers Kyle J

机构信息

National Institute of Biomedical Imaging and Bioengineering/Center for Derices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland 20993, USA.

出版信息

J Opt Soc Am A Opt Image Sci Vis. 2007 Dec;24(12):B70-80. doi: 10.1364/josaa.24.000b70.

Abstract

Multireader multicase (MRMC) variance analysis has become widely utilized to analyze observer studies for which the summary measure is the area under the receiver operating characteristic (ROC) curve. We extend MRMC variance analysis to binary data and also to generic study designs in which every reader may not interpret every case. A subset of the fundamental moments central to MRMC variance analysis of the area under the ROC curve (AUC) is found to be required. Through multiple simulation configurations, we compare our unbiased variance estimates to naïve estimates across a range of study designs, average percent correct, and numbers of readers and cases.

摘要

多读者多病例(MRMC)方差分析已被广泛用于分析观察者研究,其中汇总指标是受试者操作特征(ROC)曲线下的面积。我们将MRMC方差分析扩展到二元数据以及通用研究设计,在这些设计中,并非每个读者都要解读每个病例。结果发现,对于ROC曲线下面积(AUC)的MRMC方差分析,只需用到一部分基本矩。通过多种模拟配置,我们将无偏方差估计与一系列研究设计、平均正确百分比以及读者和病例数量下的简单估计进行了比较。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验