In one set of simulation experiments I am finding that the Rubin
variance-covariance formula works very well for regression imputation
but that the standard error of the final regression coefficient for a
frequently missing target variable is very much underestimated if PMM is
used. Does anyone have experience with this or know of a pertinent
reference? In doing PMM I have used both the closest match as part of
the random-draw multiple imputation algorithm, and I have also tried
weighted sampling where the closest match has the highest probability of
being selected but donors around the closest may be selected with
decreasing probability as they are farther away from the closest match.
Missingness of the target variable is moderately strongly related to
observed values of another covariate (that has no missings).
Thanks
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
