Advertisement
Table 4 Summary of the missing data methods investigated
| Method Label | Method Description | Library used within R statistical software | Number of iterations |
|---|---|---|---|
| CC | Complete case analysis: Analyses only cases with complete data for all covariates | - | |
| SI | Single imputation performed using PMM | 'pmm' function in 'mice' | 20 |
| MI-NORM | Multiple imputation (MI) using data augmentation approach [31] with a multivariate normal assumption for all variables | 'norm' [41] | 100 |
| MI-MIX | MI using data augmentation approach using a general location model | 'mix' [42] | 100 |
| MI-MIX-no truncating | MI using data augmentation approach using a general location model, but imputed values are not truncated to within plausible range | 'mix' [42] | 100 |
| MI-MICE | MI using regression switching imputation [9]. Linear model are used for continuous covariates and logistic model for binary covariates and dummy variables for categorical covariates | 'mice' [43] | 20 |
| MI-MICE-PMM | MI using MICE with PMM | 'pmm' function in 'mice' [43] | 20 |
| MI-MICE-PMM-no transformation | MI using MICE with PMM without transforming the incomplete covariates | 'pmm' function in 'mice' [43] | 20 |
| MI-Aregimpute | MI using flexible additive imputation models [20] with PMM | 'aregImpute' function in 'Hmisc' [44] | 1 |
