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

Feng Dai (), Baumgartner Richard () and Svetnik Vladimir ()
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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

The International Journal of Biostatistics, 2018, vol. 14, issue 1, 8

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.

Keywords: concordance correlation coefficient; multivariate normal and t distributions; multivariate skew normal and t distributions; MCMC (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/ijb-2017-0050

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