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Predictive variables with some association with vaccination (p<0.05) were tested in multivariate models using forward stepwise multivariable logistic regression and the likelihood ratio method.
All variables that are p<0.15 in the GLMM univariate analyses will be entered into multivariate models using a stepwise approach.
All variables with P < 0.10 in univariate analyses were entered into the multivariate logistic model using stepwise selection.
We selected variables for inclusion in models using a stepwise forward selection procedure.
Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model.
We entered these variables in the multivariate logistic model using a stepwise strategy.
Variables associated in the univariate analysis with the intention to recommend the vaccine at p ≤ 0.20 were entered into the multivariate regression models using the stepwise selection technique.
Predictive variables, if significantly associated (P < 0.05) with the dependent variable in simple linear regression analysis, were included in multivariate model with forward stepwise selection.
The association of selected variables with outcome was assessed with a univariate Cox proportional hazards model, and the variables that were significant in the univariate model were then entered into a multivariate Cox model using a forward stepwise method.
Multivariate analysis was carried out by multiple regression analysis using stepwise selection method.
Variables with a probability value ≤0.10 in univariate analyses were included in the multivariate analysis using a stepwise method.
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