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Assessing agreement with intraclass correlation coefficient and concordance correlation coefficient for data with repeated measures

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  • Chen, Chia-Cheng
  • Barnhart, Huiman X.

Abstract

The intraclass correlation coefficient and the concordance correlation coefficient are two popular scaled indices for assessing the closeness between observers who make measurements for quantitative responses. These two indices are usually based on subject and observer effects only, and therefore we cannot use these indices if the observer produces repeated measurements rather than replicated readings. In this paper, we consider not only subject and observer effects, but also time effects for data with repeated measurements since it is difficult to obtain the true replications in practice. We compare these two agreement indices for different combinations of random or fixed effects of observer and time. Finally, we use image data of 2D-echocardiograms to illustrate the proposed methodology and the comparison of these two indices. If there is a need to choose between these two indices for repeated measurements, we recommend to use the new concordance correlation coefficient since it does not need ANOVA assumptions.

Suggested Citation

  • Chen, Chia-Cheng & Barnhart, Huiman X., 2013. "Assessing agreement with intraclass correlation coefficient and concordance correlation coefficient for data with repeated measures," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 132-145.
    • Handle: RePEc:eee:csdana:v:60:y:2013:i:c:p:132-145
    DOI: 10.1016/j.csda.2012.11.004
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    References listed on IDEAS

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    1. Tony Vangeneugden & Annouschka Laenen & Helena Geys & Didier Renard & Geert Molenberghs, 2005. "Applying Concepts of Generalizability Theory on Clinical Trial Data to Investigate Sources of Variation and Their Impact on Reliability," Biometrics, The International Biometric Society, vol. 61(1), pages 295-304, March.
    2. Chen, Chia-Cheng & Barnhart, Huiman X., 2008. "Comparison of ICC and CCC for assessing agreement for data without and with replications," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 554-564, December.
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    Cited by:

    1. Thiago de Paula Oliveira & John Hinde & Silvio Sandoval Zocchi, 2018. "Longitudinal Concordance Correlation Function Based on Variance Components: An Application in Fruit Color Analysis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 233-254, June.
    2. Cacciotti, Gabriella & Hayton, James C. & Mitchell, J. Robert & Allen, David G., 2020. "Entrepreneurial fear of failure: Scale development and validation," Journal of Business Venturing, Elsevier, vol. 35(5).

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