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A logistic regression model with backwards elimination was used to predict disease outcomes by the variation of LBP levels at 48 hours (transformed into a simple 2 status categorical variable: increase or no-increase), adjusting for age, gender, number of organ failures, ARDS and APACHE II score.
a Multivariate analysis of covariance model with backwards elimination.
A multivariate analysis of covariance model with backwards elimination was used to model predictors of beliefs about medicine.
Multivariate analysis of factors significant in bivariate analysis began with a saturated model, with backwards deletion excluding the weakest association, until only significant associations remained.
In the multiple logistic regression model with backwards elimination, only age and aminotransferase (AST) were independently associated with a positive HCV antibody result (Table 3).
Hierarchical model with backwards elimination (final model n = 1874) Note: Bolded variables are in the final model and are adjusted to each other.
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Finally, the unique contribution of factors was examined in two multivariate logistic regression models with backwards elimination of non-significant terms.
To adjust for gestational age and other potential confounders (selected based on an association with the outcome at α < 0.05), we used logistic regression models with backwards stepwise elimination of non-significant variables.
The associations between background and behavioural decisions on ANC attendance and perinatal outcomes were explored using univariate analysis and multivariate logistic regression models with backwards-stepwise elimination.
Models were built with backwards stepwise model building.
We used general linear models with a backwards step-wise selection procedure with variable retention based on an α of 0.05.
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