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Furthermore, the bootstrap model selection routine gave additional information on model stability.
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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.
Yet the addition of a bootstrap model selection procedure showed that the MI-5 model might still be unstable.
Furthermore, the practical utility of the additional information provided by the bootstrap model selection procedure in prognostic modeling is demonstrated.
When fewer potential predictors are retained after the first step of the bootstrap model selection procedure, this variability seemed to decrease and model selection frequency increased.
In each of the five imputed data sets the two step bootstrap model selection procedure as described above was applied.
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.
When a variable was selected in each of the replications, the inclusion frequency was 100% * - outcome measure Tables 4, 5, 6 and 7 show the results of assessing model stability by the bootstrap model selection procedure.
Once the remote sensing derived data had been summarized to each sample size, an exhaustive model selection routine was run to find the best model assuming the best model was defined using Bayes Information Criteria BICC) [73, 74].
A model selection routine within the CAPTURE program was used to rank each of the potential models.
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