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Estimating the Generalized Concordance Correlation Coefficient through Variance Components
Author(s) -
Carrasco Josep L.,
Jover Lluís
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2003.00099.x
Subject(s) - intraclass correlation , statistics , covariate , variance (accounting) , concordance , concordance correlation coefficient , mathematics , moment (physics) , correlation coefficient , correlation , variance inflation factor , confounding , scale (ratio) , measure (data warehouse) , linear regression , computer science , data mining , multicollinearity , reproducibility , medicine , physics , geometry , accounting , classical mechanics , quantum mechanics , business
Summary . The intraclass correlation coefficient (ICC) and the concordance correlation coefficient (CCC) are two of the most popular measures of agreement for variables measured on a continuous scale. Here, we demonstrate that ICC and CCC are the same measure of agreement estimated in two ways: by the variance components procedure and by the moment method. We propose estimating the CCC using variance components of a mixed effects model, instead of the common method of moments. With the variance components approach, the CCC can easily be extended to more than two observers, and adjusted using confounding covariates, by incorporating them in the mixed model. A simulation study is carried out to compare the variance components approach with the moment method. The importance of adjusting by confounding covariates is illustrated with a case example.