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线性混合模型中肯沃德-罗杰 Wald 检验的一种幂逼近。

A power approximation for the Kenward and Roger Wald test in the linear mixed model.

机构信息

Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, CO, United States of America.

LEAD Center, University of Colorado Denver, Aurora, CO, United States of America.

出版信息

PLoS One. 2021 Jul 21;16(7):e0254811. doi: 10.1371/journal.pone.0254811. eCollection 2021.

DOI:10.1371/journal.pone.0254811
PMID:34288958
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8294572/
Abstract

We derive a noncentral [Formula: see text] power approximation for the Kenward and Roger test. We use a method of moments approach to form an approximate distribution for the Kenward and Roger scaled Wald statistic, under the alternative. The result depends on the approximate moments of the unscaled Wald statistic. Via Monte Carlo simulation, we demonstrate that the new power approximation is accurate for cluster randomized trials and longitudinal study designs. The method retains accuracy for small sample sizes, even in the presence of missing data. We illustrate the method with a power calculation for an unbalanced group-randomized trial in oral cancer prevention.

摘要

我们推导出肯沃德-罗杰检验的非中心[公式：见正文]幂逼近。在备择假设下，我们使用矩量法方法来形成肯沃德-罗杰缩放 Wald 统计量的近似分布。结果取决于未缩放 Wald 统计量的近似矩。通过蒙特卡罗模拟，我们证明了新的功效逼近对于聚类随机试验和纵向研究设计是准确的。该方法即使在存在缺失数据的情况下，对于小样本量也能保持准确性。我们通过口腔癌预防的不平衡分组随机试验的功效计算来说明该方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f73f/8294572/7c80c724a745/pone.0254811.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f73f/8294572/ed15b41569cf/pone.0254811.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f73f/8294572/24a3fd48e171/pone.0254811.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f73f/8294572/7c80c724a745/pone.0254811.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f73f/8294572/ed15b41569cf/pone.0254811.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f73f/8294572/24a3fd48e171/pone.0254811.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f73f/8294572/7c80c724a745/pone.0254811.g003.jpg

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