Fan Chunpeng, Zhang Donghui, Zhang Cun-Hui
Department of Biostatistics and Programming, sanofi-aventis BX2-416A, 200 Crossing Boulevard, P.O. Box 6890, Bridgewater, New Jersey 08807-0890, USA.
Biometrics. 2011 Mar;67(1):213-24. doi: 10.1111/j.1541-0420.2010.01407.x.
As the nonparametric generalization of the one-way analysis of variance model, the Kruskal-Wallis test applies when the goal is to test the difference between multiple samples and the underlying population distributions are nonnormal or unknown. Although the Kruskal-Wallis test has been widely used for data analysis, power and sample size methods for this test have been investigated to a much lesser extent. This article proposes new power and sample size calculation methods for the Kruskal-Wallis test based on the pilot study in either a completely nonparametric model or a semiparametric location model. No assumption is made on the shape of the underlying population distributions. Simulation results show that, in terms of sample size calculation for the Kruskal-Wallis test, the proposed methods are more reliable and preferable to some more traditional methods. A mouse peritoneal cavity study is used to demonstrate the application of the methods.
作为单因素方差分析模型的非参数推广,当目标是检验多个样本之间的差异且基础总体分布为非正态或未知时,可应用Kruskal-Wallis检验。尽管Kruskal-Wallis检验已广泛用于数据分析,但其功效和样本量方法的研究程度要小得多。本文基于完全非参数模型或半参数位置模型中的预试验,提出了Kruskal-Wallis检验的新功效和样本量计算方法。对基础总体分布的形状不做任何假设。模拟结果表明,在Kruskal-Wallis检验的样本量计算方面,所提出的方法比一些更传统的方法更可靠且更可取。通过一项小鼠腹腔研究来证明这些方法的应用。