hi Jillian
Take a look at this paper - it may give you some ideas, and talks about a macro MMI_IMPUTE.
Having said that, I've poked around here and found a thread asking a very similar question; the author of the paper, who as of 2017 worked for SAS, posted "...I no longer recommend using MMI_IMPUTE. MMI_IMPUTE uses an imputation algorithm called PAN, which was developed a while back by Joseph Schafer. Unfortunately, PAN is a bit outdated, and is less flexible than newer algorithms (e.g., the algorithm can't incorporate random effects between incomplete variables; all incomplete variables are required to be normally distributed). I recommend that you instead use a standalone software package called Blimp. Blimp was written by Brian Keller and Craig Enders at UCLA. Unlike MMI_IMPUTE, Blimp can handle random effects between incomplete variables and can also handle some non-normal incomplete variables (e.g., binary variables). The software and associated documentation are available at http://www.appliedmissingdata.com/multilevel-imputation.html. Blimp is free, and the website contains scripts for using it from SAS."
I don't know Blimp at all, but as Missing Data / Imputation is something I'm interested in, I will definitely be taking a look. Please post back if you have any further questions; I'm also happy to contact you via email if that's easier.
Chris
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