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Our study showed that the application of this two-step bootstrap model selection procedure provides valuable information on model stability.
A two-step bootstrap model selection procedure [ 9, 11] was applied to provide information on model stability.
Furthermore, the practical utility of the additional information provided by the bootstrap model selection procedure in prognostic modeling is demonstrated.
In each of the five imputed data sets the two step bootstrap model selection procedure as described above was applied.
Yet the addition of a bootstrap model selection procedure showed that the MI-5 model might still be unstable.
As illustrated by our study, the bootstrap model selection procedure may provide valuable additional information on model stability when deriving a prognostic model in multiple imputed data.
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Furthermore, the bootstrap model selection routine gave additional information on model stability.
For each bootstrap sample, a stepwise backward logistic model selection procedure, starting with all the features of a specific feature group, was carried out to obtain the best model according to the Akaike information criterion.
The best model was selected using model selection procedure (AICc).
We used a bootstrap approach to evaluate the impact of this model selection procedure on variability in the regression parameters for the food predictors.
The inference for this causal effect estimate was obtained by bootstrap (without consideration of additional variability introduced by the model selection procedure as is the case with virtually all reports of conventional analyses).
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