Generalized Concordance Correlation Coefficient Based on the Variance Components Generalized Linear Mixed Models for Overdispersed Count Data

• Published in: Biometrics Vol. 66; no. 3; pp. 897 - 904
• Main Author: Carrasco, Josep L.
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.09.2010; Wiley-Blackwell; Blackwell Publishing Ltd
• Subjects: Agreement; Analysis of Variance; Antigens, CD34; BIOMETRIC PRACTICE; Biometrics; biometry; Bone marrow; Cell Count; Clinical Laboratory Techniques - statistics & numerical data; Computer Simulation; Concordance correlation coefficient; correlation; Correlation analysis; Correlation coefficients; Dispersion; Estimate reliability; Estimation methods; Generalized linear mixed models; Humans; Leukapheresis; Linear Models; Linguistic agreement; Models, Statistical; Negative binomial model; Overdispersion; Poisson model; Sample Size; Standard error; Statistical variance; variance
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/j.1541-0420.2009.01335.x

The classical concordance correlation coefficient (CCC) to measure agreement among a set of observers assumes data to be distributed as normal and a linear relationship between the mean and the subject and observer effects. Here, the CCC is generalized to afford any distribution from the exponential family by means of the generalized linear mixed models (GLMMs) theory and applied to the case of overdispersed count data. An example of CD34+ cell count data is provided to show the applicability of the procedure. In the latter case, different CCCs are defined and applied to the data by changing the GLMM that fits the data. A simulation study is carried out to explore the behavior of the procedure with a small and moderate sample size.