Spearman和Kendall系数的样本量图表

Justin May, S. Looney
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引用次数: 31

摘要

双变量相关分析是最常用的统计方法之一。不幸的是,在计划使用相关分析的研究时,通常很少或根本没有注意到样本量的确定。例如,我们对临床研究期刊的回顾表明,在2014年发表的111篇文章中,没有一篇提供了相关结果,为相关分析中使用的样本量提供了理由。有许多易于使用的工具可用于确定基于Pearson相关系数的推理所需的样本量;然而,我们无法找到任何广泛可用的工具,可用于计算Spearman相关系数或Kendall一致性系数的样本量。在本文中,我们提供了公式和图表,可用于确定基于这些系数的推理所需的样本大小。补充资料中提供了额外的样本大小图表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sample Size Charts for Spearman and Kendall Coefficients
Bivariate correlation analysis is one of the most commonly used statistical methods. Unfortunately, it is generally the case that little or no attention is given to sample size determination when planning a study in which correlation analysis will be used. For example, our review of clinical research journals indicated that none of the 111 articles published in 2014 that presented correlation results provided a justification for the sample size used in the correlation analysis. There are a number of easily accessible tools that can be used to determine the required sample size for inference based on a Pearson correlation coefficient; however, we were unable to locate any widely available tools that can be used for sample size calculations for a Spearman correlation coefficient or a Kendall coefficient of concordance. In this article, we provide formulas and charts that can be used to determine the required sample size for inference based on either of these coefficients. Additional sample size charts are provided in the Supplementary Materials.
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