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Abstract Details
Activity Number:
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523
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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Abstract - #305921 |
Title:
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Assessing Correlation of Clustered Mixed Outcomes from a Multivariate Generalized Linear Mixed Model
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Author(s):
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Hsiang-chun Chen*+ and Thomas E. Wehrly
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Companies:
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Texas A&M University and Texas A&M University
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Address:
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Department of Statistics, Texas A&M University, College Station, TX, 77843, United States
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Keywords:
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clustered data ;
multivariate generalized linear mixed model ;
correlation
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Abstract:
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The classic concordance correlation coefficient measures the agreement between two variables. In recent study, 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. The relationship among these responses is often of interest. In the clustered mixed data, the correlation could present between repeated measurements either within the same observer or between different observers on the same subjects. This study proposes a series of indices, intra, inter and total correlation coefficients, to measure the correlation under various circumstances in a multivariate generalized linear mixed model, especially for joint modeling of clustered count and continuous outcomes. The proposed indices are natural extension of the concordance correlation coefficient. We demonstrate the methodology with a simulation study. A case example is provided to illustrate the use of these proposed indices.
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