Literature DB >> 25355470

Power calculation for comparing diagnostic accuracies in a multi-reader, multi-test design.

Eunhee Kim1, Zheng Zhang, Youdan Wang, Donglin Zeng.   

Abstract

Receiver operating characteristic (ROC) analysis is widely used to evaluate the performance of diagnostic tests with continuous or ordinal responses. A popular study design for assessing the accuracy of diagnostic tests involves multiple readers interpreting multiple diagnostic test results, called the multi-reader, multi-test design. Although several different approaches to analyzing data from this design exist, few methods have discussed the sample size and power issues. In this article, we develop a power formula to compare the correlated areas under the ROC curves (AUC) in a multi-reader, multi-test design. We present a nonparametric approach to estimate and compare the correlated AUCs by extending DeLong et al.'s (1988, Biometrics 44, 837-845) approach. A power formula is derived based on the asymptotic distribution of the nonparametric AUCs. Simulation studies are conducted to demonstrate the performance of the proposed power formula and an example is provided to illustrate the proposed procedure.
© 2014, The International Biometric Society.

Entities:  

Keywords:  Multi-reader; Multi-test design; Power; Receiver operating characteristic curve; Sample size; U-statistics

Mesh:

Year:  2014        PMID: 25355470      PMCID: PMC4305439          DOI: 10.1111/biom.12240

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  23 in total

1.  Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis.

Authors:  S V Beiden; R F Wagner; G Campbell
Journal:  Acad Radiol       Date:  2000-05       Impact factor: 3.173

2.  A non-parametric method for the comparison of partial areas under ROC curves and its application to large health care data sets.

Authors:  Dong D Zhang; Xia-Hua Zhou; Daniel H Freeman; Jean L Freeman
Journal:  Stat Med       Date:  2002-03-15       Impact factor: 2.373

3.  Partial AUC estimation and regression.

Authors:  Lori E Dodd; Margaret S Pepe
Journal:  Biometrics       Date:  2003-09       Impact factor: 2.571

4.  Multireader, multicase receiver operating characteristic analysis: an empirical comparison of five methods.

Authors:  Nancy A Obuchowski; Sergey V Beiden; Kevin S Berbaum; Stephen L Hillis; Hemant Ishwaran; Hae Hiang Song; Robert F Wagner
Journal:  Acad Radiol       Date:  2004-09       Impact factor: 3.173

5.  A comparison of denominator degrees of freedom methods for multiple observer ROC analysis.

Authors:  Stephen L Hillis
Journal:  Stat Med       Date:  2007-02-10       Impact factor: 2.373

6.  One-shot estimate of MRMC variance: AUC.

Authors:  Brandon D Gallas
Journal:  Acad Radiol       Date:  2006-03       Impact factor: 3.173

7.  Nonparametric statistical inference method for partial areas under receiver operating characteristic curves, with application to genomic studies.

Authors:  Yaohua He; Michael Escobar
Journal:  Stat Med       Date:  2008-11-10       Impact factor: 2.373

8.  Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis.

Authors:  Stephen L Hillis; Kevin S Berbaum; Charles E Metz
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

9.  Analysis of correlated ROC areas in diagnostic testing.

Authors:  H H Song
Journal:  Biometrics       Date:  1997-03       Impact factor: 2.571

10.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

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