Multiple imputation is an important and influential approach for dealing with the statistical

analysis of incomplete data, when missingness occurs both in the outcome and in the covariates$\backslash$cite$\{$molenberghs$2005$models$\}.$The fundamental concept behind the multiple imputation $($MI$)$ procedure is to substitute each missing value with a set of M plausible values.Each value is drawn from the conditional distribution of the missing observation, given the observed data.The imputations generate M "completed" datasets,where each is analyzed using standard procedures.

M parameter estimates and standard errors are obtained from the analysis, which are then combined to a single inference, i$.$e$,$ single estimates and single standard errors are obtained using the Rubin rules. Since there are M imputations and the fact that they are drawn randomly from the predictive distribution, the variability between the imputations is used to correct the precision estimates, so that the amount of information is not artificially increased.MI is particularly effective under Missing At Random $($MAR$),$ and although most software implementations assume MAR, it can also be applied in Missing Not At Random $($MNAR$)$ settings, especially with certain pattern$-$mixture models.A range of imputations is preferred, ideally $210,$ as results tend to be fairly stable beyond $10$ imputations. A suite of different M$$s i$.$e$,$ M$=2,5,10,15$ were run and, it was observed that the results were reasonably similar. Therefore, $5$ imputations were selected for our analysis which is based on the understanding that using more imputations is important for gain of precision.Imputation was conducted on the continuous response, which was later trichotomized within each of the $5$ complete datasets. This was done to This ensure that the imputation model uses the maximum information contents available.$\backslash \backslash$

oindent In addition, the approach appears to have some benefits over its competitors,for example, the WGEE and the direct likelihood, which permit multiple analyses of the same data and is applicable to longitudinal investigations that are hierarchical.As It is able to handle incomplete covariates. Additionally, the technique can be applied to data with missing mechanisms of the Missing Not at Random type. Help me rephrase the above statement using different wordings but maintain the same meaning.