Missing values on the number of tests per month and the number of receptions were imputed using multivariate imputation by chained equations (MICE) and implemented by the R package 'mice 2.9'mice
Missing information was imputed using multivariate imputation by chained equation method [ 34].
Missing data were imputed using multivariate imputation by chained equations in Stata v11.0 [ 39– 41].
Missing data for covariates were imputed using Multivariate Imputation by Chained Equations based on 20 cycles [ 24].
In order to determine the impact of the missing values within the PreVILIG database on the performance and accuracy of the prediction models, missing data were imputed by means of multiple imputation using "Multivariate Imputation by Chained Equations" (MICE) [ 24].
Since using complete case analysis and not taking missing data into account can result in biased estimates, 34 an approach was taken to impute missing data using multivariate imputation by chained equations via the ice method 35 in Stata 12.
In order to minimise selection bias and to reduce variability in the model estimates, we imputed missing data using multivariate imputation by chained equations (MICE) 19 in the statistical package R (open source Comprehensive R Archive Network (CRAN) package MICE).
Fifteen (10 training, 5 validation) samples were imputed using multivariate imputation chain equations.
We then imputed five complete data sets using multivariate imputation by chained equations, 27 28 including all the covariables listed above, as well as the prevalent and incident measures of centre volume and patient outcomes in the imputation model.
Missing ranks were then replaced using multivariate imputation by chained equations (MICE; van Buuren & Groothuis-Oudshoorn, 2011; van Buuren & Oudshoorn, 2000).
