|
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
|
