Abstract Details
Activity Number:
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687
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Type:
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Contributed
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Date/Time:
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Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #309683 |
Title:
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A Robust Bayesian Estimate of the Concordance Correlation Coefficient
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Author(s):
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Dai Feng*+ and Richard Baumgartner and Vladmir Svetnik
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Companies:
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Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc.
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Keywords:
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concordance correlation coefficient ;
robust estimate ;
Bayesian MCMC ;
Multivariate t distribution ;
Jackknife ;
Bootstrap
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Abstract:
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Assessment of agreement arises in many situations including statistical biomarker qualification or assay or method validation. Concordance correlation coefficient (CCC) is one of the most popular scaled indices reported in evaluation of agreement. The CCC is typically computed using assumptions of normality and development of robust methods for CCC estimation presents currently an important statistical challenge. In this contribution, we propose a novel Bayesian method of robust estimation of CCC based on multivariate student's t distribution and compare it with its alternatives. Furthermore, we extend the method for practically relevant settings, enabling incorporation of confounding covariates and replications. The superiority of the new approach is demonstrated using simulation as well as real data sets from biomarker application in Electroencephalography (EEG). This biomarker is relevant in neuroscience in development of treatments for insomnia.
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Authors who are presenting talks have a * after their name.
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