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Patients with missing data for any of the variables included in the models were dropped from the analyses.
Variables that showed no significant impact on asthma diagnosis in either tabular analysis or when analyzed in univariate logistic regression models were dropped from future analyses.
When other non-significant components of the models were dropped, all of these effects of depression severity on these 4 mRNAs were significant (p<0.03).
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Helmet models were drop-tested from five heights to assess how much force they allow to reach the skull; the lower that force, the lower the risk of concussion, and the better the helmet scored.
The non-significant parameter (either A or C) of the ACE models was dropped and the model fit of the nested models (AC or AE models) was compared to that of the full ACE models to further clarify the roles of genetics and environment in the aforementioned skills.
Variables that were not significant at p < 0.05 in the model were dropped.
Therefore, variables that did not significantly change the fit of the final model were dropped.
With the exception of age and gender, variables that were no longer significant in the multivariate model were dropped.
Assessments missing either the outcome or more than half of the predictor variables included in the final model were dropped.
Initially, all interactions were included in the model, but interactions not contributing significantly to the model were dropped from subsequent analyses.
Fixed effects from the individual year analyses were checked again for significance in the combined model, and those which became insignificant in the combined model were dropped.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com