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In each model, non-significant factors (p ≥ 0.05) were eliminated by backward stepwise method.
Those risk factors with a P value less than 0.2 in bivariate analysis were selected to enter into the multivariate model and the final model was constructed by backward stepwise method with P values of 0.1 set as the significance level for removal from the model.
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All variables were entered into the multiple logistic regression model by the backward stepwise method.
Cox regression analysis was applied by the backward stepwise method with CKD as the dependent variable.
Covariates for inclusion were selected by the backward stepwise method with a significance criterion for removal from the model of P > 0.25.
A school-level minimal adequate logistic regression model was developed by the backward stepwise method, whereby variables with P > 0.05 were removed in the order of least significance; all excluded variables were subsequently retested in the final model.
To evaluate the risk factors related to the presence of drug-drug interactions in prescription mixes, a multiple logistic regression analysis by using the backward stepwise method was performed; the correlation terms and interactions among selected variables were also explored, and goodness of fit test was assessed for the best model.
The relations between the measured variables and mortality or massive bleeding were analyzed by stepwise logistic regression analysis (the backward stepwise method based on likelihood) with the use of death or massive bleeding as dependent variables.
We calculated the odds ratios (OR) and 95% confidence intervals (CI) by using logistic regression analysis with the backward stepwise method.
The backward stepwise method (Wald) was used.
The backward stepwise method was used to create the models.
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