To test for association between genetic markers and total serum levels of log-adjusted IgE and log-adjusted EASI score, we used linear regression models adjusting for confounding variables age and gender as well as the first two principal components.
After adjusting for confounding variables, the difference was found to be marginally significant (adjusted OR, 0.59; 95% CI 0.29 to 1.21; p=0.1.21
Then, a multivariate logistic regression model was used to test the association of colonization with mortality, adjusting for confounding variables such as the SAPS II score.
This remained significant after adjusting for confounding variables in analysis of the deployment questionnaire and health screening form.
General linear model analysis was undertaken to test for associations between SNP genotypes and IMT and HDL level after adjusting for confounding variables.
Logistic regression analyses adjusting for confounding variables, smoking and gravidity, were then performed on the most significant associations (i.e., the best of the additive, dominant, or recessive model for those markers with p≤10−3).
The latter association emerged only after adjusting for confounding variables.
This was the case after adjusting for confounding variables.
These associations were not present after adjusting for confounding variables in our multivariable model.
We tested associations using mixed-effects regression models, adjusting for confounding variables.
After adjusting for confounding variables, the significance remained for HbA1c, predicted CHD and stroke risks.
