A generalized concordance correlation coefficient based on the variance components generalized linear mixed models for overdispersed count data.

Abstract:

: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.

journal_name

Biometrics

journal_title

Biometrics

authors

Carrasco JL

doi

10.1111/j.1541-0420.2009.01335.x

subject

Has Abstract

pub_date

2010-09-01 00:00:00

pages

897-904

issue

3

eissn

0006-341X

issn

1541-0420

pii

BIOM1335

journal_volume

66

pub_type

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