Multiple Imputation requires the modelling of incomplete data under formal assumptions about the combined model for observed and unobserved data (the imputation model).
Generalized Linear Mixed Models provide a natural framework for modelling repeated observations, especially for non-Gaussian outcomes. The new BGLIMM procedure in SAS/Stat 15.1 fits a wide range of such models allowing for missing data under MAR assumption. The posterior output data set includes the Bayesian sampled values for the missed values. These are exactly what are needed to complete the imputed data sets. This SAS macro BGI automatically merges the input data set with the multiple posterior missed values to a generate a single MI data set indexed by the variable _IMPUTATION_. This can then be multiply analyzed by the user and a summary built using Rubin’s rules.
The design vector required for each imputed value is specified in the call to proc BGLIMM just as if it were observed. This allows complex models such as treatment switching.
The macro requires access to SAS/Stat 15.1 or later.
The macro can be downloaded here BGI20190226