Summary-statistics-based power analysis: A new and practical method to determine sample size for mixed-effects modelling
Summary-statistics-based power analysis: A new and practical method to determine sample size for mixed-effects modelling
复制标题
基于汇总统计的功效分析：一种确定混合效应建模样本量的实用新方法
DOI：
10.31219/osf.io/6cer3
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
M. Sakaki
中科院分区：
文献类型：
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作者：
K. Murayama;S. Usami;M. Sakaki
本文提出了一种基于汇总统计的功率分析方法-一种实用的两级嵌套数据混合效应建模的功率分析方法(包括二值和连续预测)，补充了现有的基于公式和基于模拟的方法。该方法的逻辑基于汇总统计法和混合效应模型的条件等价性，将混合效应模型的功率分析缩减为更简单的统计分析(例如，单样本t检验)。因此，建议的方法允许我们使用流行的软件，如G*Power或R中的PWR程序包进行混合效果建模的功率分析，并且在规划2级样本量时，需要从相关先前工作中获得最少的输入(例如，t值)。我们给出了分析证明和一系列统计模拟，以显示基于汇总统计的功率分析的有效性和稳健性，并用实际出版的工作给出了一些说明性的例子。我们还开发了一个Web应用程序(https://koumurayama.shinyapps.io/summary_statistics_based_power/)来促进所提出的方法的实用性。虽然与现有方法相比，该方法在可适当处理的模型和设计方面的灵活性有限，但它为应用研究人员在信息有限的情况下进行功率分析提供了一种方便的选择。
This article proposes a summary-statistics-based power analysis --- a practical method for conducting power analysis for mixed-effects modelling with two-level nested data (for both binary and continuous predictors), complementing the existing formula-based and simulation-based methods. The proposed method bases its logic on conditional equivalence of the summary-statistics approach and mixed-effects modelling, paring back the power analysis for mixed-effects modelling to that for a simpler statistical analysis (e.g., one-sample t test). Accordingly, the proposed method allows us to conduct power analysis for mixed-effects modelling using popular software such as G*Power or the pwr package in R and, when planning level 2 sample size, requires minimum input from relevant prior work (e.g., t value). We provide analytic proof and a series of statistical simulations to show the validity and robustness of the summary-statistics-based power analysis and show some illustrative examples with real published work. We also developed a web app (https://koumurayama.shinyapps.io/summary_statistics_based_power/) to facilitate the utility of the proposed method. While the proposed method has limited flexibilities compared to the existing methods in terms of the models and designs that can be appropriately handled, it provides a convenient alternative for applied researchers when there is limited information to conduct power analysis.