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A Bayesian Framework for Estimating the Concordance Correlation Coefficient Using Skew-elliptical Distributions

Author

Listed:
  • Feng Dai

    (Merck & Co., Inc, Rahway, NJ, United States of America)

  • Baumgartner Richard

    (Merck & Co., Inc, Rahway, NJ, United States of America)

  • Svetnik Vladimir

    (Merck & Co., Inc, Rahway, NJ, United States of America)

Abstract

The concordance correlation coefficient (CCC) is a widely used scaled index in the study of agreement. In this article, we propose estimating the CCC by a unified Bayesian framework that can (1) accommodate symmetric or asymmetric and light- or heavy-tailed data; (2) select model from several candidates; and (3) address other issues frequently encountered in practice such as confounding covariates and missing data. The performance of the proposal was studied and demonstrated using simulated as well as real-life biomarker data from a clinical study of an insomnia drug. The implementation of the proposal is accessible through a package in the Comprehensive R Archive Network.

Suggested Citation

  • Feng Dai & Baumgartner Richard & Svetnik Vladimir, 2018. "A Bayesian Framework for Estimating the Concordance Correlation Coefficient Using Skew-elliptical Distributions," The International Journal of Biostatistics, De Gruyter, vol. 14(1), pages 1-8, May.
    • Handle: RePEc:bpj:ijbist:v:14:y:2018:i:1:p:8:n:5
    DOI: 10.1515/ijb-2017-0050
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    References listed on IDEAS

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    1. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
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