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Hidden multiplicity in the analysis of variance (ANOVA): multiple contrast tests as an alternative

Ludwig A. Hothorn
doi: https://doi.org/10.1101/2022.01.15.476452
Ludwig A. Hothorn
Leibniz University Hannover, Germany
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Abstract

In bio-medical studies, the p-values of the F-tests in ANOVA are usually interpreted independently as measures of the significance of the associated factors. This ’hidden multiplicity’ effect increases the false positive rate. Therefore, Cramer et al. (2016) proposed the Bonferroni adjustment of the p-values to control for familywise error rate for the experiment. Here, instead of using F-tests, it is alternatively suggested to use multiple contrast tests vs. total mean and to perform multiplicity adjustment by object merging in the interplay between the R-packages emmeans and multcomp. This new approach, denotes as multipleANOM, allows not only to interpret global factor effects but also local effects between factor levels as adjusted p-values or simultaneous confidence intervals for selected effect measures in generalized linear models. R-code is provided by means of selected data examples.

Competing Interest Statement

The authors have declared no competing interest.

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  • (ludwig{at}hothorn.de)

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 18, 2022.
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Hidden multiplicity in the analysis of variance (ANOVA): multiple contrast tests as an alternative
Ludwig A. Hothorn
bioRxiv 2022.01.15.476452; doi: https://doi.org/10.1101/2022.01.15.476452
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Hidden multiplicity in the analysis of variance (ANOVA): multiple contrast tests as an alternative
Ludwig A. Hothorn
bioRxiv 2022.01.15.476452; doi: https://doi.org/10.1101/2022.01.15.476452

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