Instead, analysts increasingly favour multiple imputation, where multivariate regression techniques are used to predict missing values on the basis of existing data.
Fifteen (10 training, 5 validation) samples were imputed using multivariate imputation chain equations.
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].
All missing data were multiply imputed via the Multivariate Imputation by Chained Equations (MICE) procedure [ 21].
Missing values were imputed using a Multivariate Imputation by Chained Equations (MICE) procedure with the "predictive mean matching" as imputation method [ 30].
2. Parametric imputation where albumin is imputed from a normal errors linear model.
Multiple imputation was performed in the R package MICE (Multivariate Imputation using Chained Equations) 33 providing 20 completed data sets with values imputed where these were missing.
11 Missing data was imputed by the method of multivariate imputation using chained equations.
