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Imputation models sometimes use auxiliary variables that, though not part ofthe planned analysis, can improve the accuracy of imputed values and theefficiency of point estimates. A recent article, using evidence fromsimulations, argued that the use of auxiliary variables in imputation did notimprove efficiency. We review the simulation results and find that the use ofauxiliary variables did improve efficiency; under some conditions theefficiency gain was equivalent to increasing the sample size by a quarter. Wegive an example from our own research where the efficiency gained fromauxiliary variables was equivalent to increasing the sample size by threequarters, and pushed some estimates from statistical insignificance tosignificance. For auxiliary variables to make a difference, there must be a lotof missing data, some estimates must be near the border of significance, andthe auxiliary variables must be excellent predictors of the missing values.

Efficiency Gains from Using Auxiliary Variables in Imputation