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Additionally, we evaluated this model while controlling for marital status and sexual orientation.
This group variable was entered into the model while controlling for age, gender, admission SBP, and admission GFR.
The multivariate analyses made it possible to measure each variable's contribution to the analysis model while controlling for covariables.
Next, these bivariate associations with poor SRH need to be tested simultaneously in one model while controlling for potential confounding factors in multiple regression analyses.
These effects were significant also when entered in the same model, while controlling for age (OR 0.32, CI95 0.12-0.83, p = 0.019, Table 3).
The Odds Ratio (OR) between the outcomes and obesity and their 95% confidential intervals (95%CI) were estimated in logistic regression model while controlling for other factors.
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Statistical significance was tested in multivariate models, while controlling for age, gender, race/ethnicity and marital status.
In addition, we verified the results by substituting the BDI for group membership in our models while controlling for age, although this modification led to clearly larger AICs.
Adjusted odds ratios for adverse outcomes were determined by multiple logistic regression models, while controlling for the following factors: maternal age, parity, and education.
In our analysis, we will estimate this association via hierarchical regression models, while controlling for various possible other important factors as well as confounding factors.
Much work is needed to independently measure mutational biases in many organisms and to improve the efficiency and fidelity of the selection models, while controlling model complexity and avoiding overfitting.
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