The intraclass correlation coefficient (ICC) is a classical index of measurement reliability. With the advent of new and complex types of data for which the ICC is not defined, there is a need for new ways to assess reliability. To meet this need, we propose a new distance-based intraclass correlation coefficient (dbICC), defined in terms of arbitrary distances among observations. We introduce a bias correction to improve the coverage of bootstrap confidence intervals for the dbICC, and demonstrate its efficacy via simulation. We illustrate the proposed method by analyzing the test-retest reliability of brain connectivity matrices derived from a set of repeated functional magnetic resonance imaging scans. The Spearman-Brown formula, which shows how more intensive measurement increases reliability, is extended to encompass the dbICC. This article is protected by copyright. All rights reserved.

中文翻译：

---

基于距离的广义可靠性

组内相关系数 (ICC) 是测量可靠性的经典指标。随着未定义 ICC 的新型复杂数据类型的出现，需要新的方法来评估可靠性。为了满足这一需求，我们提出了一种新的基于距离的类内相关系数 (dbICC)，根据观察之间的任意距离定义。我们引入了偏差校正以提高 dbICC 的引导置信区间的覆盖范围，并通过模拟证明其有效性。我们通过分析从一组重复的功能磁共振成像扫描得出的大脑连接矩阵的重测可靠性来说明所提出的方法。Spearman-Brown 公式显示了更密集的测量如何提高可靠性，扩展到包含 dbICC。本文受版权保护。版权所有。