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A backward stepwise selection approach was used for model construction.
A backward, stepwise selection approach using covariates found to be significant by univariate analysis was employed in the modeling strategy.
The multivariable (adjusted) model was fitted with a backward stepwise selection approach, which included type of procedure by default and any other variables that were significant (as conservatively defined by P<0.1).
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A backward stepwise selection procedure was used to select significant independent variables.
Only variables which showed an univariable association (p ≤ 0.20) were selected for the backward stepwise selection.
The most appropriate model was chosen using forward and backward stepwise selection.
Backward stepwise selection procedures were used to retain proposed variables in each final model.
A backward stepwise selection of variables based on the Schwarz Bayesian information criterion (BIC) was applied to reduce overfitting [25] [28].
To obtain the final set of variables included in the model we used a backward stepwise selection procedure [54].
Backward stepwise selection was performed.
Backward stepwise selection procedure was used.
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