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automatic backward selection was applied to the first 2,000 data sets from M100+B method.
A moderate backward selection was applied in order to exclude factors that were not relevant.
automatic backward selection was applied by drawing 200 bootstrap samples from the first imputed dataset only.
automatic backward selection was applied to the 20,000 data sets from the nested procedure.
Variables with P-values <0.20 were then included in the multivariate competing risk model, and a backward selection was applied.
To investigate the development of DLCO over regular follow-up time, mixed model analysis with backward selection was applied.
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Finally, using Akaike's information criterion for covariate selection, a backward stepwise selection was applied.
In each model, backward variable selection was applied using the Akaike Information Criterion (AIC) (13).
In this model, backward stepwise selection was applied removing variables with a P value >.05; thus, variables that did not influence the results were excluded.
Bivariate analysis was performed with all factors and covariates, and then backward stepwise selection was applied in multivariate weighted generalized estimating equation models, starting with all variables that reached P < 0.2 in the bivariate analysis.
When several related variables were tested (e.g., diet or physical activity), a backward model selection was applied to remove non-significant (α >5%) effects from the mixed regression model.
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