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The final model was constructed using a manual backward stepwise elimination procedure (p > 0.20): we eliminated from the model the predictors one-by-one on the basis of the highest p-value.
A backward stepwise elimination procedure based on the likelihood statistics, (using probability of 0.1 for removal and 0.05 for entry) was also performed to identify the best subset of variables as risk factors.
Subsequently, reduced models are estimated, dropping non-significant variables by using a backward stepwise elimination procedure.
We generated a simplified model by means of a backward stepwise elimination procedure.
The final logistic regression model was constructed by using the backward stepwise elimination procedure, removing predictors with p>0.05.
Variables were retained when the P-value was < 0.05 using a backward stepwise elimination procedure as for the Step I model.
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*Significant In the backward stepwise elimination process only three variables were retained in the model.
Covariates with P < 0.1 were included in a multivariate logistic regression model (LBW, anaemia) or linear regression (birthweight) as a starting model for a backward stepwise elimination model selection procedure and considered independent risk factors if P < 0.05.
Backward (Wald) stepwise elimination procedures were conducted to include all variables showing an overall p<0.20 in the regression model (33).
Non-significant terms were backward dropped using a stepwise elimination procedure.
I performed model selection using backward stepwise elimination.
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