This article proposes a summary-statistics-based power analysis—a practical method for conducting power analysis for mixed-effects modeling 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 modeling, paring back the power analysis for mixed-effects modeling 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 modeling using popular software such as G*Power or the pwr package in R and, with 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 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 with 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.

中文翻译：

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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。

本文提出了一种基于汇总统计的功效分析——一种对具有两级嵌套数据（适用于二元和连续预测变量）的混合效应建模进行功效分析的实用方法，补充了现有的基于公式和基于模拟的方法. 所提出的方法将其逻辑基于汇总统计方法和混合效应建模的条件等价性，将混合效应建模的功效分析缩减为更简单的统计分析（例如，单样本 t 检验）。因此，所提出的方法允许我们使用流行的软件（例如G*Power或 R 中的 pwr 包）对混合效应建模进行功率分析，并且使用相关先前工作的最少输入（例如，t价值）。我们提供分析证明和一系列统计模拟，以展示基于汇总统计的功效分析的有效性和稳健性，并展示真实发表作品的说明性示例。我们还开发了一个网络应用程序 (https://koumurayama.shinyapps.io/summary_statistics_based_power/) 以促进所提出方法的实用性。虽然与现有方法相比，所提出的方法在可以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提供了一种方便的替代方法，用于进行功率分析的信息有限。