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

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...

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Autores principales:	Sarah M Kreidler, Brandy M Ringham, Keith E Muller, Deborah H Glueck
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b1e26b13d0d641eb804b9a3ac3bc691c
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spelling	oai:doaj.org-article:b1e26b13d0d641eb804b9a3ac3bc691c2021-12-02T20:06:42ZA power approximation for the Kenward and Roger Wald test in the linear mixed model.1932-620310.1371/journal.pone.0254811https://doaj.org/article/b1e26b13d0d641eb804b9a3ac3bc691c2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254811https://doaj.org/toc/1932-6203We 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.Sarah M KreidlerBrandy M RinghamKeith E MullerDeborah H GlueckPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254811 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sarah M Kreidler Brandy M Ringham Keith E Muller Deborah H Glueck A power approximation for the Kenward and Roger Wald test in the linear mixed model.
description	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.
format	article
author	Sarah M Kreidler Brandy M Ringham Keith E Muller Deborah H Glueck
author_facet	Sarah M Kreidler Brandy M Ringham Keith E Muller Deborah H Glueck
author_sort	Sarah M Kreidler
title	A power approximation for the Kenward and Roger Wald test in the linear mixed model.
title_short	A power approximation for the Kenward and Roger Wald test in the linear mixed model.
title_full	A power approximation for the Kenward and Roger Wald test in the linear mixed model.
title_fullStr	A power approximation for the Kenward and Roger Wald test in the linear mixed model.
title_full_unstemmed	A power approximation for the Kenward and Roger Wald test in the linear mixed model.
title_sort	power approximation for the kenward and roger wald test in the linear mixed model.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b1e26b13d0d641eb804b9a3ac3bc691c
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A power approximation for the Kenward and Roger Wald test in the linear mixed model.

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