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Significant variables in the univariate analysis were included in a multivariate model (conditional logistic regression).
The covariables were selected by stepwise algorithm in a multivariate model.
Significant variables in the univariate analysis were included in a multivariate model (logistic regression).
In a multivariate model, knowledge of evolutionary terms was predicted by education and religious denomination.
In a multivariate model, AF was a negative prognostic factor for functional outcome in stroke patients.
However, predictive statements should only be made in combination with other variables in a multivariate model.
As many of these characteristics are interdependent, we examined these relationship both univariately and in a multivariate model.
We assessed each determinant separately (in models that included age, gender, and education) and then jointly in a multivariate model.
To analyze potential predictors to the different TMD-diagnoses in a multivariate model logistic regression was used to calculate OR.
We included the strongest clinical risk factors from the univariate analysis along with copeptin in a multivariate model.
In a multivariate model, continuation of statin therapy (P = 0.008) was an independent predictor of favorable outcome.
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