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Parametric versus non-parametric statistics in the analysis of randomized trials with non-normally distributed data

机译:非正态分布数据的随机试验分析中的参数统计与非参数统计

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摘要

BackgroundIt has generally been argued that parametric statistics should not be applied to data with non-normal distributions. Empirical research has demonstrated that Mann-Whitney generally has greater power than the t-test unless data are sampled from the normal. In the case of randomized trials, we are typically interested in how an endpoint, such as blood pressure or pain, changes following treatment. Such trials should be analyzed using ANCOVA, rather than t-test. The objectives of this study were: a) to compare the relative power of Mann-Whitney and ANCOVA; b) to determine whether ANCOVA provides an unbiased estimate for the difference between groups; c) to investigate the distribution of change scores between repeat assessments of a non-normally distributed variable.
机译:背景技术人们普遍认为,参数统计不应应用于具有非正态分布的数据。实证研究表明,除非从正常样本中抽取数据,否则曼恩·惠特尼通常比t检验具有更大的功效。对于随机试验,我们通常对治疗后终点(例如血压或疼痛)如何变化感兴趣。此类试验应使用ANCOVA进行分析,而不是t检验。这项研究的目的是:a)比较Mann-Whitney和ANCOVA的相对能力; b)确定ANCOVA是否为组间差异提供了无偏估计; c)研究非正态分布变量的重复评估之间变化得分的分布。

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