Literature DB >> 22733707

Homogeneity tests of clustered diagnostic markers with applications to the BioCycle Study.

Liansheng Larry Tang1, Aiyi Liu, Enrique F Schisterman, Xiao-Hua Zhou, Catherine Chun-Ling Liu.   

Abstract

Diagnostic trials often require the use of a homogeneity test among several markers. Such a test may be necessary to determine the power both during the design phase and in the initial analysis stage. However, no formal method is available for the power and sample size calculation when the number of markers is greater than two and marker measurements are clustered in subjects. This article presents two procedures for testing the accuracy among clustered diagnostic markers. The first procedure is a test of homogeneity among continuous markers based on a global null hypothesis of the same accuracy. The result under the alternative provides the explicit distribution for the power and sample size calculation. The second procedure is a simultaneous pairwise comparison test based on weighted areas under the receiver operating characteristic curves. This test is particularly useful if a global difference among markers is found by the homogeneity test. We apply our procedures to the BioCycle Study designed to assess and compare the accuracy of hormone and oxidative stress markers in distinguishing women with ovulatory menstrual cycles from those without.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 22733707      PMCID: PMC4084872          DOI: 10.1002/sim.5391

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  9 in total

1.  Comparison of correlated receiver operating characteristic curves derived from repeated diagnostic test data.

Authors:  K H Zou
Journal:  Acad Radiol       Date:  2001-03       Impact factor: 3.173

2.  Combining several screening tests: optimality of the risk score.

Authors:  Martin W McIntosh; Margaret Sullivan Pepe
Journal:  Biometrics       Date:  2002-09       Impact factor: 2.571

3.  Nonparametric and semiparametric group sequential methods for comparing accuracy of diagnostic tests.

Authors:  Liansheng Tang; Scott S Emerson; Xiao-Hua Zhou
Journal:  Biometrics       Date:  2008-03-27       Impact factor: 2.571

4.  Smooth non-parametric receiver operating characteristic (ROC) curves for continuous diagnostic tests.

Authors:  K H Zou; W J Hall; D E Shapiro
Journal:  Stat Med       Date:  1997-10-15       Impact factor: 2.373

5.  Nonparametric analysis of clustered ROC curve data.

Authors:  N A Obuchowski
Journal:  Biometrics       Date:  1997-06       Impact factor: 2.571

6.  Influence of endogenous reproductive hormones on F2-isoprostane levels in premenopausal women: the BioCycle Study.

Authors:  Enrique F Schisterman; Audrey J Gaskins; Sunni L Mumford; Richard W Browne; Edwina Yeung; Maurizio Trevisan; Mary Hediger; Cuilin Zhang; Neil J Perkins; Kathleen Hovey; Jean Wactawski-Wende
Journal:  Am J Epidemiol       Date:  2010-08-01       Impact factor: 4.897

7.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

Authors:  E R DeLong; D M DeLong; D L Clarke-Pearson
Journal:  Biometrics       Date:  1988-09       Impact factor: 2.571

8.  Identifying combinations of cancer markers for further study as triggers of early intervention.

Authors:  S G Baker
Journal:  Biometrics       Date:  2000-12       Impact factor: 2.571

9.  BioCycle study: design of the longitudinal study of the oxidative stress and hormone variation during the menstrual cycle.

Authors:  Jean Wactawski-Wende; Enrique F Schisterman; Kathleen M Hovey; Penelope P Howards; Richard W Browne; Mary Hediger; Aiyi Liu; Maurizio Trevisan
Journal:  Paediatr Perinat Epidemiol       Date:  2009-03       Impact factor: 3.980

  9 in total
  2 in total

1.  Estimating the AUC with a Graphical Lasso Method for High-dimensional Biomarkers with LOD.

Authors:  Jirui Wang; Yunpeng Zhao; Liansheng Larry Tang
Journal:  Biostat Epidemiol       Date:  2021-03-17

2.  Least squares regression methods for clustered ROC data with discrete covariates.

Authors:  Liansheng Larry Tang; Wei Zhang; Qizhai Li; Xuan Ye; Leighton Chan
Journal:  Biom J       Date:  2016-02-05       Impact factor: 2.207

  2 in total

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