Five imputations were created using a set of appropriate imputation models constructed of all co-variables and outcome variables, stratified by year categories.
Although imputation procedures were applied to fill data gaps to enable comparative analyses, the selection of an appropriate imputation method and subsequent validation of its application were challenging, especially because as much as 70% of the stage data was missing in some cases.
Nevertheless, the development of appropriate imputation techniques could solve the problem.
17 Five imputations were created by using a set of appropriate imputation models constructed from all covariates and outcome variables, stratified by year epochs.
If data are missing to a sufficient extent, the use of appropriate imputation techniques will be considered to allow for this in the analysis.
If not, we shall use appropriate imputation methods to ameliorate the problem of missing data.
With an appropriate imputation model, multiple imputation is an unbiased method of accounting for missing data.
This method is considered more appropriate for the imputation of categorical data because it does not assume normality of imputed variables [ 22].
However, true ITT analysis requires appropriate assumptions and imputation of missing data.
It has the advantage of a shorter processing time and was found to be an appropriate method for imputation of missing values in other studies [ 36].
