  When fitting a generalized linear model -- such as a linear regression, a
logistic regression, or a hierarchical linear model -- analysts often wonder
how to handle missing values of the dependent variable Y. If missing values
have been filled in using multiple imputation, the usual advice is to use the
imputed Y values in analysis. We show, however, that using imputed Ys can add
needless noise to the estimates. Better estimates can usually be obtained using
a modified strategy that we call multiple imputation, then deletion (MID).
Under MID, all cases are used for imputation, but following imputation cases
with imputed Y values are excluded from the analysis. When there is something
wrong with the imputed Y values, MID protects the estimates from the
problematic imputations. And when the imputed Y values are acceptable, MID
usually offers somewhat more efficient estimates than an ordinary MI strategy.
