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

Carrasco, Josep L.

doi:10.1111/j.1541-0420.2009.01335.x

Cited by 45 publications

(54 citation statements)

References 23 publications

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“…The intra-CC measures the within-method correlation, the inter-CC measures the between-method correlation, and the total-CC measures the overall correlation. These indices are natural extensions of the intra-CC, inter-CC, and total-CC proposed by Lin [17] and Carrasco [19] and are very useful for measuring consistency in clustered mixed data. The inter-CC and total-CC measure the marginal correlation, while the correlation of the random effect measure only the residual correlation.…”

Section: Resultsmentioning

confidence: 98%

“…The relationship of number of tumors among patients, the relationship of blood pressure among patients and the relationship between the number of tumors and blood pressure may be of interest. To access these relationships, a series of CCs are defined by extending approaches of measuring intra, inter, and total agreement [16,17,19] in the next few subsections .…”

Section: Methodsmentioning

confidence: 99%

“…Several previously proposed indices in measuring agreement are special cases of the intra-CC, inter-CC, and total-CC proposed in this study. If the methods are identically distributed, then the intra-CC, inter-CC, and total-CC reduce to the generalized intra CCC, inter CCC, and total CCC proposed by Carrasco [19]. Because the measurements are from particular distributions in the exponential family in GLMM, the normal linear mixed model with identity link is a special case of GLMM.…”

Section: Propertiesmentioning

confidence: 97%

“…However, inference based on the aforementioned measures of correlation assume that both random effects and the residuals are normally distributed, which is sometimes not appropriate. The CCC was extended by Carrasco and Jover [18,19] for assessing the agreement for data from any distribution of the exponential family in terms of the GLMM. All aforementioned indices can be used to measure agreement and evaluate the interchangeability when measurements are assumed to follow identical distributions.…”

Section: Introductionmentioning

confidence: 99%

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Assessing correlation of clustered mixed outcomes from a multivariate generalized linear mixed model

Chen

Wehrly

2014

Statistics in Medicine

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The classic concordance correlation coefficient measures the agreement between two variables. In recent studies, concordance correlation coefficients have been generalized to deal with responses from a distribution from the exponential family using the univariate generalized linear mixed model. Multivariate data arise when responses on the same unit are measured repeatedly by several methods. The relationship among these responses is often of interest. In clustered mixed data, the correlation could be present between repeated measurements either within the same observer or between different methods on the same subjects. Indices for measuring such association are needed. This study proposes a series of indices, namely, intra-correlation, inter-correlation, and total correlation coefficients to measure the correlation under various circumstances in a multivariate generalized linear model, especially for joint modeling of clustered count and continuous outcomes. The proposed indices are natural extensions of the concordance correlation coefficient. We demonstrate the methodology with simulation studies. A case example of osteoarthritis study is provided to illustrate the use of these proposed indices.

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Section: Resultsmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

Section: Propertiesmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing correlation of clustered mixed outcomes from a multivariate generalized linear mixed model

Chen

Wehrly

2014

Statistics in Medicine

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“…The variances of qualitative explanatory factors are estimated using sums of the squared fixed effects. This approach allows the definition of the intra-class correlation coefficients in the linear model to be transferred to the more comprehensive classes of the GLMM (Carrasco 2010).…”

Section: Calculation Of Repeatabilitymentioning

confidence: 99%

Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars

2015

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Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM). In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribution and logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars) in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negativebinomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented.

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Quantifying intraclass correlations for count and time‐to‐event data

Oliveira

Molenberghs

Demétrio³

et al. 2016

Biometrical J

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The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.

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

Cited by 45 publications

References 23 publications

Assessing correlation of clustered mixed outcomes from a multivariate generalized linear mixed model

Assessing correlation of clustered mixed outcomes from a multivariate generalized linear mixed model

Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars

Quantifying intraclass correlations for count and time‐to‐event data

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