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Given that the percent of missing data varied for each explanatory variable, multiple imputation, using chained equations, was used to impute missing data in order to check the sensitivity of missing data to the identification of significant predictors.
Because on average ∼7% data were missing for any particular variable, multiple imputation was used to fill missing information.
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For missing variables, multiple imputation was applied.
To deal with missing values of the important analytic variables multiple imputation will be carried out.
To account for missing values of some variables, multiple imputations were performed to create 10 versions of the analytic data set and we then used Rubin's combining rules to calculate the final parameter estimates and CIs from the 10 output sets (Rubin 1987).
Recurrence after transplantation was used as a predictor variable because multiple imputation with the outcome has been shown to yield more valid results [ 25].
Almost no data were missing (2% of the planned visits, and 3-73-7%ssing per variable); thus multiple imputation was deemed unnecessary.
We imputed missing values for all baseline and interim exposure variables using multiple imputation (N = 5).
We accounted for missing values of baseline variables with multiple imputation techniques [25].
More research on auxiliary variables in multiple imputation should be performed.
In order to maintain maximal sample size and retain all valid data for the LCR, we simulated missing data for all variables using multiple imputation (MI).
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