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Costs are compared between the three groups using bootstrap methods to account for the likely non-normal distribution of the data.
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In GeneSTAR, SE estimates and hypothesis testing were done using bootstrapping methods to account for within-family correlations.
We use bootstrap methods to calculate cluster-robust standard errors (Efron 1979).
Methods to account for bias.
Ninety-five perconfidencedence intervals were calculated using bootstrap methods (to take account of the two-stage assessment procedure).
The 95%% confidence intervals (CIs) were derived using exact standard errors generated through the bootstrap re-sampling method to account for the complex sampling design [ 27].
We adopted the bootstrap method to conduct regression analysis.
For this reason, we use the bootstrap method to quantify the uncertainty introduced by the ANN metamodel.
We used a bootstrap method to obtain confidence intervals [32].
We also used the bootstrap method to validate the reliability of the evolutionary tree branches with 1,000 bootstrap replications.
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].
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