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Multiple imputations are needed to properly account for the uncertainty in the imputation process and the resulting set of imputed estimates can be combined using standard multiple imputation combining rules.
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All analyses from multiple imputed data were combined using standard methods [27, 29].
Data sets were analyzed in parallel and the results were combined using standard techniques (Roudsari et al. 2008).
Programs were combined using standard bash and awk commands.
dGALT, dGALK and sgl alleles were combined using standard genetic techniques to generate double mutants.
Results (ORs) of the four studies were combined using standard fixed-effects meta-analyses.
Results from multiply imputed data sets were combined using standard multiple imputation rules (White et al. 2011).
When the costs and effects for subgroups are combined using standard decision analytic aggregation methods, the ACCG scenario is cost-saving and more effective.
Results of each trial were combined using standard meta-analytic methods to estimate an overall effect for physiotherapy versus no intervention.
The results from the COG discovery cohort and the UPDB validation cohort were combined using weighted standard errors in meta-analysis, due to differences in study design between the cohorts, to generate summary ORs (ORs) and 95% CIs.
Finally, strata-specific expected scores are combined using weights from the standard population (see example in Additional file 2).
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