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Reference based multiple imputation methods have become popular for handlingmissing data in randomised clinical trials. Rubin's variance estimator is wellknown to be biased compared to the reference based imputation estimator's truerepeated sampling variance. Somewhat surprisingly given the increasinglypopularity of these methods, there has been relatively little debate in theliterature as to whether Rubin's variance estimator or alternative (smaller)variance estimators targeting the repeated sampling variance are moreappropriate. We review the arguments made on both sides of this debate, andconclude that the repeated sampling variance is more appropriate. We reviewdifferent approaches for estimating the frequentist variance, and suggest arecent proposal for combining bootstrapping with multiple imputation as awidely applicable general solution. At the same time, in light of theconsequences of reference based assumptions for frequentist variance, webelieve further scrutiny of these methods is warranted to determine whether thethe strength of their assumptions are generally justifiable.

Reference based multiple imputation -- what is the right variance and how to estimate it