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.

中文翻译：

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使用斜椭圆分布估计一致性相关系数的贝叶斯框架。

一致性相关系数 (CCC) 是一致性研究中广泛使用的标度指标。在本文中，我们建议通过一个统一的贝叶斯框架来估计 CCC，该框架可以（1）适应对称或不对称以及轻尾或重尾数据；(2) 从几个候选者中选择模型；(3) 解决实践中经常遇到的其他问题，例如混淆协变量和缺失数据。使用来自失眠药物临床研究的模拟和现实生物标志物数据研究和证明了该提案的性能。该提案的实施可通过综合 R 档案网络中的一个包访问。