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Concordance correlation coefficients estimated by variance components for longitudinal normal and Poisson data

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  • Tsai, Miao-Yu
  • Lin, Chao-Chun

Abstract

The concordance correlation coefficient (CCC) is widely used to assess agreement between two observers for continuous responses. Further, the CCC is extended for measuring agreement with discrete data. This paper proposes a variance components (VC) approach that allows dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously under extended three-way generalized linear mixed-effects models (GLMMs) for longitudinal normal and Poisson data. Furthermore, we propose a weight matrix to compare with existing weight matrices. Simulation studies are conducted to compare the performance of the VC, generalized estimating equations and U-statistics approaches with different weight matrices for repeated measurements from longitudinal normal and Poisson data. Two applications, of myopia twin and of corticospinal diffusion tensor tractography studies, are used for illustration. In conclusion, the VC approach with consideration of the correlation structure of longitudinal repeated measurements gives satisfactory results with small mean square errors and nominal 95% coverage rates for all sample sizes.

Suggested Citation

  • Tsai, Miao-Yu & Lin, Chao-Chun, 2018. "Concordance correlation coefficients estimated by variance components for longitudinal normal and Poisson data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 57-70.
    • Handle: RePEc:eee:csdana:v:121:y:2018:i:c:p:57-70
    DOI: 10.1016/j.csda.2017.12.003
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    References listed on IDEAS

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    1. Huiman X. Barnhart & Michael Haber & Jingli Song, 2002. "Overall Concordance Correlation Coefficient for Evaluating Agreement Among Multiple Observers," Biometrics, The International Biometric Society, vol. 58(4), pages 1020-1027, December.
    2. Josep L. Carrasco & Lluís Jover, 2003. "Estimating the Generalized Concordance Correlation Coefficient through Variance Components," Biometrics, The International Biometric Society, vol. 59(4), pages 849-858, December.
    3. Huiman X. Barnhart & John M. Williamson, 2001. "Modeling Concordance Correlation via GEE to Evaluate Reproducibility," Biometrics, The International Biometric Society, vol. 57(3), pages 931-940, September.
    4. Josep L. Carrasco, 2010. "A Generalized Concordance Correlation Coefficient Based on the Variance Components Generalized Linear Mixed Models for Overdispersed Count Data," Biometrics, The International Biometric Society, vol. 66(3), pages 897-904, September.
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