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It also seems essential to properly study the profile of missing data in advance, so as to propose a suitable method of score imputation in case of intermittent missing items with an MNAR profile.
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A multiple imputation strategy will be used in case of missing explanatory variables.
In these power analyses, we corrected for the fact that data are clustered (children are nested within schools) and the fact that we will apply multiple imputation in the case of missing data.
For the missing data, in case of missing >0.05, imputation was done by putting the same data from the adjacent cases after sorting.
In case of missing data following imputation methods were used in order of preference.
In case of longitudinal missing data, imputation is performed with the last observation carried forward method.
In case of missing values, a multiple imputation model was used to analyze and complete the dataset.
In case of missing data a non-responder imputation will be applied (ie, baseline observation carried forward).
In case of missing values we applied the mean imputation method.
In case of a high percentage of missing values, no imputation was performed.
In case of missing covariates, due to missing questionnaire data, multiple imputation will be used [ 81].
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