Handling Missing Data in Clinical Research

Martijn W Heymans, Jos Twisk
Because missing data is present in almost every study, it is important to handle missing data properly. First of all, the missing data mechanism should be considered. Missing data can be either completely at random (MCAR), at random (MAR) or not at random (MNAR). When missing data is MCAR, a complete case analysis can be valid. Also when missing data is MAR, in some situations a complete case analysis lead to valid results. However, in most situations, missing data imputation should be used.
more » ... arding imputation methods, it is highly advised to use multiple imputation, because multiple imputation leads to valid estimates including the uncertainty about the imputed values. When missing data is MNAR, also multiple imputation does not lead to valid results. A complication hereby is that it not possible to distinguish whether missing data is MAR or MNAR. Finally, it should be realised that preventing to have missing data is always better than the treatment of missing data.
doi:10.1016/j.jclinepi.2022.08.016 pmid:36150546 fatcat:c4teaubxknei3pf2urv2u2wxdq