A Robust Bayesian Estimate of the Concordance Correlation Coefficient

Abstract

A need for assessment of agreement arises in many situations including statistical biomarker qualification or assay or method validation. Concordance correlation coefficient (CCC) is one of the most popular scaled indices reported in evaluation of agreement. Robust methods for CCC estimation currently present an important statistical challenge. Here, we propose a novel Bayesian method of robust estimation of CCC based on multivariate Student’s t-distribution and compare it with its alternatives. Furthermore, we extend the method to practically relevant settings, enabling incorporation of confounding covariates and replications. The superiority of the new approach is demonstrated using simulation as well as real datasets from biomarker application in electroencephalography (EEG). This biomarker is relevant in neuroscience for development of treatments for insomnia.

Key words:

• Bayesian MCMC
• Bootstrap
• Concordance correlation coefficient
• Jackknife
• Multivariate t-distribution
• Robust estimate

ACKNOWLEDGMENTS

We would like to acknowledge helpful comments and suggestions of the anonymous reviewers that improved the article. We would like to also thank Dr. Elizabeth Rosenberg for editorial help.