A Comparison of Different Imputation Strategies for Missing At Random Data

Abstract In multiple imputation, the resulting estimates are consistent if the im-putation model is correct. To specify the imputation model, it is recommended to combine two sets of variables, those that are related to the incomplete variable and those that are related to the missingness mechanism. Several possibilities exist, but it is not clear how they perform in practice. The method that simply groups all vari-ables together into the imputation model and four other methods that are based on the propensity scores are presented. Two of them are new and have not been used in the context of multiple imputation. The performance of the methods is investigated by a simulation study under different MAR mechanisms for different types of vari-ables. We conclude that all methods, except for one method based on the propensity scores, perform well. It turns out that as long as the relevant variables are taken into the imputation model, the form of the imputation model has only a minor effect in the quality of the imputations