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For the ITT approach, missing data were imputed via multiple imputation using chained equations; 20 imputed datasets were computed on guidance that the number needed should approximate the percentage of incomplete cases.
Missing covariate data were imputed with multiple imputation using IVEwareE [ 16].
In the base case analysis missing data were imputed by multiple imputation using chained equations (see web appendix) [ 11].
12 16 17 40 To address missing data on education, we conducted multiple imputation using five imputed data sets according to methods developed by Rubin.
Missing data were imputed using MICE (multiple imputation using chained equations) [ 21].
These values were imputed using a multiple imputation using the aregImpute function from the Hmisc library in R, using additive regression, bootstrapping and predictive mean matching [ 19].
Missing cost data, for example, from patients who did not consent to access to their MBS, PBS, and public hospital data will be imputed using the Multiple Imputation using Chained Equations (MICE) approach.
Missing values in the possible determinants were imputed using the MICE technique (Multiple Imputation Using Chained Equations).
To evaluate a potential effect of missing values on the observed results, we used multiple imputation using chained equations with 30 imputated data sets (White et al, 2011).
Multiple imputation using chained equations was used to impute missing covariates; five imputations were generated.
To reduce potential selection bias a missing data technique (multiple imputation using chained equations) was used to impute the missing confounder values 11 only.
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Justyna Jupowicz-Kozak
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