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Abstract Details
Activity Number:
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511
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #305891 |
Title:
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Tutorial: Statistical Methods for Meta-Analysis of Diagnostic Tests
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Author(s):
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Xiaoye Ma*+ and Haitao Chu and Lei Nie and Stephen Cole
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Companies:
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University of Minnesota and University of Minnesota and FDA/CDER/OTS and The University of North Carolina at Chapel Hill
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Address:
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1627 CARL ST, SAINT PAUL, MN, 55108, United States
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Keywords:
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meta-analysis ;
diagnostic test ;
gold standard ;
the summary receiver operating characteristics approach ;
the hierarchical summary receiver operating characteristics approach ;
generalized linear mixed models
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Abstract:
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We consider statistical methods for meta-analysis of diagnostic tests under two scenarios: 1) when the reference test can be considered as a gold standard; and 2) when the reference test cannot be considered as a gold standard. In scenario 1, we first review the conventional summary receiver operating characteristics (SROC) approach and a bivariate approach using linear mixed models (BLMM). Both methods require direct calculations of sensitivities and specificities for each study. We next discuss the hierarchical SROC approach for jointly modeling positivity criteria and accuracy parameters, and the bivariate generalized linear mixed models (GLMM) for jointly modeling sensitivities and specificities. We further discuss the trivariate GLMM for jointly modeling prevalence, sensitivities and specificities, which allows us to assess the correlations among the three parameters. Those methods are based on the exact binomial distribution and thus do not require any ad hoc continuity correction. At last, we discuss statistical methods for meta-analysis of diagnostic tests when the reference test itself is imperfect. We present a latent class random effects model for this scenario.
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