%0 Journal Article
%A Hothorn, Ludwig A.
%T Hidden multiplicity in the analysis of variance (ANOVA): multiple contrast tests as an alternative
%D 2022
%R 10.1101/2022.01.15.476452
%J bioRxiv
%P 2022.01.15.476452
%X 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 â€™hidden multiplicityâ€™ 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 StatementThe authors have declared no competing interest.
%U https://www.biorxiv.org/content/biorxiv/early/2022/01/18/2022.01.15.476452.full.pdf