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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0254811 |
| _version_ | 1818718929554505728 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T19:58:51Z |
| format | Article |
| id | doaj.art-b1e26b13d0d641eb804b9a3ac3bc691c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T19:58:51Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b1e26b13d0d641eb804b9a3ac3bc691c2022-12-21T21:34:31ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01167e025481110.1371/journal.pone.0254811A power approximation for the Kenward and Roger Wald test in the linear mixed model.Sarah M KreidlerBrandy M RinghamKeith E MullerDeborah H GlueckWe 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.https://doi.org/10.1371/journal.pone.0254811 |
| spellingShingle | 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. PLoS ONE |
| title | 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_short | 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 |
| url | https://doi.org/10.1371/journal.pone.0254811 |
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