  Reference based multiple imputation methods have become popular for handling
missing data in randomised clinical trials. Rubin's variance estimator is well
known to be biased compared to the reference based imputation estimator's true
repeated sampling variance. Somewhat surprisingly given the increasingly
popularity of these methods, there has been relatively little debate in the
literature as to whether Rubin's variance estimator or alternative (smaller)
variance estimators targeting the repeated sampling variance are more
appropriate. We review the arguments made on both sides of this debate, and
conclude that the repeated sampling variance is more appropriate. We review
different approaches for estimating the frequentist variance, and suggest a
recent proposal for combining bootstrapping with multiple imputation as a
widely applicable general solution. At the same time, in light of the
consequences of reference based assumptions for frequentist variance, we
believe further scrutiny of these methods is warranted to determine whether the
the strength of their assumptions are generally justifiable.
