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We use 10k bootstrap samples to estimate the confidence interval of the correlation coefficient.
Therefore, we used bootstrap samples to estimate a 'shrinkage factor' (between 0 and 1) [ 31].
As a measure of internal validation, we used 100 bootstrap samples to estimate the AUC and Nagelkerke's R for the final models.
Due to the limited number of reticular naevi (n = 62) in our analyses, we used a bootstrap procedure with 200 bootstrap samples to estimate the standard errors of the odds ratios, and obtained the corresponding P-values.
In our study, the mediaton hypothesis was tested with SPSS macro (the INDIRECT procedure) for bootstrapping (with 5000 bootstrap samples) to estimate 95% confidence interval (CI; for more details, see Preacher and Hayes [ 36]).
Table 5 shows the regression coefficients of the 2013 DYD on the 2007 EBV in the VAL set (standard errors are based on 10,000 bootstrap samples) to estimate biases of the different methods.
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57 We also used the 5000 bootstrapped samples to estimate cost effectiveness acceptability curves.
We used bootstrap methods, with 100 bootstrap samples, to calculate an optimism-corrected estimate of each measure of predictive accuracy (Efron and Tibshirani, 1993; Steyerberg, 2009).
We used bootstrapping, with 5000 samples, to estimate costs, QALYs (adjusted for baseline EQ-5D scores), and change in the vertigo symptom scale-short form.
We used the rescaled bootstrap method with 1,000 samples to estimate the 95% confidence intervals of integrated discrimination improvement and net reclassification improvement (39).
Currently, we use permutation test to assess the statistical significance of LS scores and bootstrap re-sampling to estimate the confidence interval of LS score.
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