2009-01-15, English, Article, Journal or magazine article edition: Nonparametric analysis of clustered data in diagnostic trials: Estimation problems in small sample sizes. Konietschke, Frank; Brunner, Edgar

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Physical Description
  • Magazine/​Journal article
Published
  • Elsevier B.V., 2009 Jan 15
Language
  • English

Edition details

Title
  • Nonparametric analysis of clustered data in diagnostic trials: Estimation problems in small sample sizes.
Appears In
  • Computational Statistics and Data Analysis, v.53, no.3, 2009 Jan 15, p.730(12) (ISSN: 0167-9473)
Author
  • Konietschke, Frank
  • Brunner, Edgar
Published
  • Elsevier B.V., 2009 Jan 15
Physical Description
  • Magazine/​Journal article
Subjects
Target Audience
  • Academic
Summary
  • To link to full-text access for this article, visit this link: http:/​/​dx.doi.org/​10.1016/​j.csda.2008.08.006 Byline: Frank Konietschke, Edgar Brunner Abstract: In diagnostic trials, clustered data are obtained when several subunits (e.g., organs or vessels) of the same patient are observed where no, several, or all subunits may be diseased or non-diseased as classified by a gold standard. In such a design, repeated measures appear in a natural way since the same patient is observed under different conditions by several readers and the repeated measures may have a quite involved correlation structure. A nonparametric method for clustered data in multiple reader studies to estimate the area under the ROC curve has been previously considered. The disadvantage of this procedure is that the test statistic (a quadratic form) can become negative in case of small samples. Therefore, a slightly different approach by weighting the estimators of the areas under the curves (AUC) is proposed. It is shown that the proposed new estimator of the covariance matrix of the weighted AUC estimators is always positive semidefinite. Simulation studies show that the new statistic maintains the pre-assigned type-I error level quite well even in case of small sample sizes. The method is motivated by a real data example where the previously suggested statistic becomes negative. This example demonstrates the advantage of the new method. Author Affiliation: Department of Medical Statistics, University of Gottingen, Germany
Terms of Use
  • COPYRIGHT 2009 Elsevier B.V.
Language
  • English
Database
  • Gale Academic OneFile
Identifier
  • 350827196

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