A Generalized Concordance Correlation Coefficient Based on the Variance Components Generalized Linear Mixed Models for Overdispersed Count Data
Article first published online: 10 OCT 2009
© 2009, The International Biometric Society
Volume 66, Issue 3, pages 897–904, September 2010
How to Cite
Carrasco, J. L. (2010), A Generalized Concordance Correlation Coefficient Based on the Variance Components Generalized Linear Mixed Models for Overdispersed Count Data. Biometrics, 66: 897–904. doi: 10.1111/j.1541-0420.2009.01335.x
- Issue published online: 10 OCT 2009
- Article first published online: 10 OCT 2009
- Received October 2008. Revised July 2009. Accepted July 2009.
- Concordance correlation coefficient;
- Generalized linear mixed models;
- Negative binomial model;
- Poisson model
Summary 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.