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We employed a backward stepwise selection algorithm to find the model with minimum Akaike Information Criterion (AIC), 32 retaining age and gender as the minimum model.
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The regression coefficients along with their 95%% CIs, and the bootstrap inclusion frequencies of infependent variables for the models selected using automatic stepwise selection algorithm (backward elimination and forward selection) with AIC, BIC and the Likelihood ratio test (p = 0.05).
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.
Forward and backward stepwise selection procedures were used.
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