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Bootstrap methods were used to estimate the difference in median changes and associated 95% confidence intervals.
Bootstrap methods were used to estimate the respective 95% confidence intervals[ 21].
Bootstrap methods were used to estimate the variability of τ ^ 1 by re-sampling with replacement 400 data sets each with 10,000 employees [ 24].
Bootstrap methods were used to determine confidence intervals for these incremental cost-effectiveness ratio's.Two scenarios of 15 and 30% were used to express the association between increased HH compliance and the reduction in HAIs The TDS was significantly more effective in improving HH compliance.
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Missing costs and QALY data were imputed using a regression-based method adjusting for baseline variables costs, EQ-5D-3L scores, GHQ-12 score, and the type of MS. Non-parametric bootstrapping methods were used to account for non-normality in the distribution of cost data [27].
Bootstrapping methods were used for self-reported "heart attack".
Bootstrapping methods were used to calculate 95% confidence intervals (55).
Bootstrapping methods were used to estimate the variability around the estimates.
Bootstrapping methods were used when more than two categories or weighted kappa were involved; to optimize the results, bootstrapping was performed with 5000 replications.
Bootstrapping methods were used in the linear regression model to verify whether the association was robust after multiple sampling procedures had been applied to the models.
In all models, individuals were weighted based on their sampling weight in the CCHS, and bootstrapping methods were used to determine 95% confidence intervals around all estimates.
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