title: Assessing correlation of clustered mixed outcomes from a multivariate generalized linear mixed model.
abstract: 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.
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A.
authors.forename: Hsiang-Chun
authors.initials: HC
authors.lastname: Chen
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Wehrly
mesh_terms: D056808:Biostatistics / Q000379:methods*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008297:Male; D015999:Multivariate Analysis; D020370:Osteoarthritis, Knee / Q000209:etiology; D016012:Poisson Distribution; D012307:Risk Factors
keywords: clustered mixed outcome; correlation coefficient; multivariate generalized linear model; repeated measurements