We begin by examining the theoretical underpinnings of multiple imputation and then briefly describe traditional imputation approaches.
The complementary approach of multiple imputation can then be applied to assess uncertainty due to the imputation process.
Multiple imputation is then an adequate approach to handle missing data.
Depending on the simulated condition, this was either directly followed by fitting the linear regression model using the CC method, or by first creating multiple imputations and then following with regression analyses.
A multiple imputation approach was then used to assign income quintile, taking the average of the multiple imputed values, [ 38] for all DAs that did not have missing information on one or more of these socio-demographic variables and for all individuals who did not have a missing postal code.
A multiple imputation approach was then used to assign income quintile [ 37].
31 Then we used multiple imputation to impute missing data in THIN.
The multiple imputation strategy developed was then validated on the 30% validation sample.
If MVs can be considered as ignorable, then multiple imputation is recommended.
If the proportion of missingness in the records is low then multiple imputation can be used [ 6].
