Estimation and inference are two key components toward the solution of any statistical problem; however, the inferential issues of statistical assessment of agreement among two or more raters have not been well developed as compared to the development of estimation procedures in this area. The fundamental reason for this gap is the complex expression of the concordance correlation coefficient (CCC) that is frequently used in assessing agreement among raters. Large sample‐based statistical tests for CCC often fail to produce desired results for small samples. Hence, inferential procedures for small samples are urgently needed to evaluate agreement between raters. We argue that hypothesis testing of CCC has little value in practice due to the absence of a gold standard of agreement. In this article, we construct the generalized confidence interval (GCI) for CCC utilizing a bivariate normal distribution of measurements, and also develop a large sample‐based confidence interval (LSCI). We establish satisfactory performance of GCI by providing the desired coverage probability (CP) via simulation. Results of GCI and LSCI are illustrated and compared with a data set of a recent study performed at U.S. Department of Veterans Affairs, Hines.

中文翻译：

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评估者之间达成协议的广义置信区间

估计和推论是解决任何统计问题的两个关键组成部分。但是，与制定该领域的估算程序相比，两个或多个评估者之间的协议统计评估的推论问题尚未得到很好的发展。造成这种差距的根本原因是一致性相关系数（CCC）的复杂表达式，该表达式经常用于评估评估者之间的一致性。基于大样本的CCC统计测试通常无法为小样本产生期望的结果。因此，迫切需要用于小样本的推论程序，以评估评估者之间的一致性。我们认为，由于缺乏协议的黄金标准，CCC的假设检验在实践中几乎没有价值。在本文中，我们利用测量的二元正态分布构造了CCC的广义置信区间（GCI），并且还开发了基于样本的大置信区间（LSCI）。通过通过仿真提供所需的覆盖率（CP），我们建立了令人满意的GCI性能。举例说明了GCI和LSCI的结果，并将其与在Hines的美国退伍军人事务部进行的一项最新研究的数据集进行了比较。