Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra‐Class Correlation Coefficient in Inter‐Rater Reliability Studies

IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Abderrahmane Bourredjem, Hervé Cardot, Hervé Devilliers
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Abstract

The agreement intra‐class correlation coefficient (ICCa) is a suitable statistical index for inter‐rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined by simulation compared to the most commonly used methods, under small, moderate and large sample size designs. Then, we deduce sample size calculation formulas, for the number of subjects and observers needed, to achieve a desired confidence interval width or an acceptable ICCa value test power and give concrete examples of their use. Finally, we propose a likelihood ratio test (LRT) to compare two ICCa's from two distinct subpopulations of patients (or raters) and study by simulation its first order risk and power properties. These methods are illustrated using data from two inter‐rater reliability studies, one in physiotherapy with 42 patients and 10 raters and the second in neonatology with 80 subjects and 14 raters. In conclusion, we made recommendations to employ the proposed confidence interval for medium to large samples combined with the quantification of the minimal required sample size at the planning step, or the posterior‐power at the analysis step, using simple dedicated formulas. Furthermore, with sufficient sizes, the proposed LRT seems suitable to compare inter‐rater reliability between two patient subpopulations. Used wisely, this proposed methods toolbox can remedy common current issues in inter‐rater reliability studies.
评分者间可靠性研究中的协议类内相关系数的渐近置信区间、样本量公式和比较测试
等内相关系数(ICCa)是一种适用于评分者间可靠性研究的统计指标。利用平衡高斯数据,我们证明了 ICCa 的渐近正态性(ASN)的明确形式,该形式在方差分析(ANOVA)、最大似然法(ML)或限制性 ML(REML)估计中均有效。然后推导出一个渐近置信区间,并在小样本量、中等样本量和大样本量设计下,通过模拟与最常用的方法进行比较,检验其性能。然后,我们推导出样本量计算公式,计算出达到所需的置信区间宽度或可接受的 ICCa 值检验功率所需的受试者和观察者数量,并给出了具体的使用示例。最后,我们提出了一种似然比检验(LRT)来比较来自两个不同亚群患者(或评分者)的两个 ICCa,并通过模拟研究了其一阶风险和功率特性。我们使用两项评分者间可靠性研究的数据对这些方法进行了说明,其中一项研究涉及物理治疗领域的 42 名患者和 10 名评分者,另一项研究涉及新生儿领域的 80 名受试者和 14 名评分者。总之,我们建议对中型到大型样本采用所建议的置信区间,并在计划步骤中量化所需的最小样本量,或在分析步骤中使用简单的专用公式量化后验功率。此外,在样本量足够大的情况下,建议的 LRT 似乎适合比较两个患者亚群之间的评分者间可靠性。明智地使用所提出的方法工具箱,可以解决当前评分者间可靠性研究中的常见问题。
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来源期刊
Statistics in Medicine
Statistics in Medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
3.40
自引率
10.00%
发文量
334
审稿时长
2-4 weeks
期刊介绍: The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
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