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Bootstrap resampling was applied to the dv/v values from 1 month before to 1 month after the beginning of the activity (Table 1).
Bootstrap resampling was applied to assess support for individual nodes using 1000 replicates.
Alternatively, bootstrap resampling was applied to compute p-values, as there was the possibility that the differences in costs were skewed.
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Internal validation with 200 bootstrap resamples was applied to the multivariable Cox regression coefficients of the nomogram predictor variables to quantify their discriminant ability according to the area under the receiver-operating characteristic curve (AUC).
The bootstrap resampling method was applied using 10,000 re-samples of the data [ 36], and bootstrap percentile confidence intervals were computed and relationships were considered as significant if 0 was outside these intervals.
Bootstrap resampling technique was applied as an internal validation tool to assess the stability of the final parameter estimates and to confirm the robustness of the final model [ 20].
Additionally, when a bootstrapping resampling technique was applied on hierarchical clustering, the cells clustered based on their p53, RB, and INK4A status.
Non-parametric balanced bootstrap resampling was used to quantify the sampling uncertainty.
Bootstrap resampling was performed for each analysis (1000 replications).
Bootstrap resampling was carried out to demonstrate robustness of groupings.
The ML analysis of 16S rRNA gene sequences (100 bootstrap resampling) was done in PhyML.
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