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ABkPowerCalculator:一款用于平衡(AB)单病例实验设计的计算功效的应用程序。

ABkPowerCalculator: An App to Compute Power for Balanced (AB) Single Case Experimental Designs.

机构信息

Daiichi Sankyo, Inc.

Texas A&M University.

出版信息

Multivariate Behav Res. 2024 Mar-Apr;59(2):406-410. doi: 10.1080/00273171.2023.2261229. Epub 2023 Oct 17.

Abstract

Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for (AB) designs. However, these computations and the software code in R may not be accessible to applied researchers who are most likely to want to compute power for their studies. Therefore, we have developed an (AB) power calculator Shiny App (https://abkpowercalculator.shinyapps.io/ABkpowercalculator/) that researchers can use with no software training. These power computations assume that the researcher would be interested in fitting multilevel models with autocorrelations or conduct similar analyses. The purpose of this software contribution is to briefly explain how power is derived for balanced (AB) designs and to elaborate on how to use the Shiny App. The app works well on not just computers but mobile phones without installing the R program. We believe this can be a valuable tool for practitioners and applied researchers who want to plan their single case studies with sufficient power to detect appropriate effect sizes.

摘要

单病例实验设计是行为和医学研究中的一种重要研究设计。尽管 What Works Clearinghouse 为单病例实验设计规定了设计标准,但这些标准不包括统计衍生的功效计算。最近,我们推导出了(AB)设计的功效计算方程。然而,这些计算和 R 中的软件代码可能无法被最有可能想要为他们的研究计算功效的应用研究人员所使用。因此,我们开发了一个(AB)功效计算器 Shiny App(https://abkpowercalculator.shinyapps.io/ABkpowercalculator/),研究人员无需软件培训即可使用。这些功效计算假设研究人员有兴趣拟合具有自相关性的多层次模型或进行类似的分析。本软件的贡献旨在简要解释如何为平衡(AB)设计推导出功效,并详细说明如何使用 Shiny App。该应用程序不仅可以在计算机上运行,而且无需安装 R 程序即可在移动电话上运行。我们相信,对于那些希望用足够的功效计划他们的单病例研究以检测适当的效果大小的从业者和应用研究人员来说,这可能是一个有价值的工具。

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