Rank methods for the analysis of clustered data in diagnostic trials

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• 作者：Carola Werner ; Edgar Brunner
• 关键词：AUC ; ROC curve ; Multivariate nonparametric Behrens– ; Fisher problem ; ANOVA-type statistic ; Multi-reader design ; Multi-modality design
• 刊名：Computational Statistics and Data Analysis
• 出版年：2007
• 出版时间：15 June 2007
• 年：2007
• 卷：51
• 期：10
• 页码：5041-5054
• 全文大小：229 K

文摘

Methods for comparing areas under receiver operating characteristic curves usually depend on the assumption of independence between diseased and nondiseased units in the trial. However, if several parts of the same subject have to be classified as diseased or nondiseased, such observations are no longer independent. This situation is referred to as clustered data. First ideas for the analysis of such data are based on the theory of U-statistics. The idea of the multivariate nonparametric Behrens–Fisher problem is extended to clustered data and to factorial designs. ANOVA-type statistics are suggested for both small and moderate sample sizes. They are evaluated in a simulation study and applied to a real-data example.