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Missing values will be addressed by multiple imputation with the propensity score method.
We therefore dedicate some attention to comparing results obtained by multiple imputation with those obtained by using single imputations made by SHAPEIT.
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In line with recommendations by Groenwold [ 76], we will perform effectiveness analyses in the following ways: analysis with covariate adjustment and 2) intention-to-treat analysis by multiple imputations with covariate adjustment.
The missing SF-6D data were imputed by multiple imputation via chained equations with the STATA 13 'mi impute' command programme to account for patients with missing data at either 3 or 6 months assuming that data were missing at random (and creating five imputed data sets).
We attend to the problem of missing data by using multiple imputation with SAS 9.2 PROC MI and MIANALYZE [ 33]; see also recommendations by Mayer et al. [ 34].
This was done by multiple imputation of missing values with the ICE routine in Stata 11.1.
Missing data will be handled by multiple imputation using regression analysis with the Markov-chain Monte Carlo (MCMC) estimation method.
We have tried to make up for this deficiency by multiple imputation, establishing a comparison with original data and showing more conservative data.
24 To reduce the computational burden, we used single imputation for the initial stages of backward selection with a cut-off P value of 0.3 before completing the selection of variables by using multiple imputation with a cut-off P value of 0.05.
In all regression analyses we accounted for missing data on covariates by using multiple imputation, with overall mortality, log transformed survival time, sex, age, smoking, CRB-65 class, comorbidities, serum glucose levels, admission to hospital, and infectious agent as variables in the imputation model, thus creating 20 imputed datasets.
In a final analysis, values were imputed for 861 children with missing data by multiple imputation by chained equations, generating 20 imputed data sets.
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