  In clinical trials, mixed effects models for repeated measures (MMRM) and
pattern mixture models (PMM) are often used to analyze longitudinal continuous
outcomes. We describe a simple missing data imputation algorithm for the MMRM
that can be easily implemented in standard statistical software packages such
as SAS PROC MI. We explore the relationship of the missing data distribution in
the control-based and delta-adjusted PMMs with that in the MMRM, and suggest an
efficient imputation algorithm for these PMMs. The unobserved values in PMMs
can be imputed by subtracting the mean difference in the posterior predictive
distributions of missing data from the imputed values in MMRM. We also suggest
a modification of the copy reference imputation procedure to avoid the
possibility that after dropout, subjects from the active treatment arm will
have better mean response trajectory than subjects who stay on the active
treatment. The proposed methods are illustrated by the analysis of an
antidepressant trial.
