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Brough, H.A., Makinson, K., Penagos, M., Maleki, S.J., Cheng, H., Douiri, A., Stephens, A.C., Turcanu, V. and Lack, G. (2013) Distribution of Peanut Protein in the Home Environment. Journal of Allergy and Clinical Immunology, 132, 623-629.
https://doi.org/10.1016/j.jaci.2013.02.035

has been cited by the following article:

• TITLE: On Sample Size Determination When Comparing Two Independent Spearman or Kendall Coefficients

  AUTHORS: Justine O. May, Stephen W. Looney

  KEYWORDS: Fisher z-Transform, Hypothesis Testing, Power, Significance Level

  JOURNAL NAME: Open Journal of Statistics, Vol.12 No.2, April 27, 2022

  ABSTRACT: One of the most commonly used statistical methods is bivariate correlation analysis. However, it is usually the case that little or no attention is given to power and sample size considerations when planning a study in which correlation will be the primary analysis. In fact, when we reviewed studies published in clinical research journals in 2014, we found that none of the 111 articles that presented results of correlation analyses included a sample size justification. It is sometimes of interest to compare two correlation coefficients between independent groups. For example, one may wish to compare diabetics and non-diabetics in terms of the correlation of systolic blood pressure with age. Tools for performing power and sample size calculations for the comparison of two independent Pearson correlation coefficients are widely available; however, we were unable to identify any easily accessible tools for power and sample size calculations when comparing two independent Spearman rank correlation coefficients or two independent Kendall coefficients of concordance. In this article, we provide formulas and charts that can be used to calculate the sample size that is needed when testing the hypothesis that two independent Spearman or Kendall coefficients are equal.