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st: Multiple Imputation in Longitudinal Multilevel Model

From   Anthony Fulginiti <>
Subject   st: Multiple Imputation in Longitudinal Multilevel Model
Date   Tue, 5 Mar 2013 19:54:50 -0800 (PST)

Dear Statalist,

I have been trying to better understand multiple imputation in the context of longitudinal multilevel modeling in Stata using Stata 12.1.  Based on my review of Stata documentation on multiple imputation, it seems as though a common choice for multiple imputation with a pattern of missing data being arbitrary rather than monotonic is using the ice command w/ subsequent MIM command. I was consulting an FAQ document from UCLA (, which provides an example of using a code strategy for MI in longitudinal data as follows:

reshape wide read math, i(id) j(time)
set seed 091107
ice female private ses read1 read2 read3 math1 math2 math3, saving(imputed_dataset) m(4)
use imputed_dataset, clear 
tab _mj
sum female private ses read1 read2 read3 math1 math2 math3
drop if _mj==0
reshape long read math, i(id _mj)
sum female private ses read math
rename _j time
mim: xtreg read math time, i(id) 
xtreg read math time if _mj==1, i(id)

My dataset has 7 datapoints and the code works fine.  However, I have 2 questions:

1) So now that I have the 4 datasets with multiply imputed values, do I have to take an additional step for model testing or when I run the models with the mim prefix, is that using the information derived from all of the multiply imputed datasets?

2) My understanding is that the xtreg command is only used for random intercept models.  However, if I am running a growth curve model with not only a random intercept but also a random slope of time, is there anything fundamentally flawed with using the xtmixed command, which I have typically used for performing growth curve analysis with a multilevel model.  When I run it, there are no error messages but that doesn't mean there aren't errors in my logic/approach/understanding so I wanted to seek your feedback.

My code looks like:
mim: xtmixed Self_Esteem c_time Ethnicity EthnicityBytime|| id: c_time, covariance(un) variance mle

My apologies for the lengthy email.  Please let me know if the addition of output would be of any help in offering advice.  I thank you in advance for your feedback.

Respectfully Yours, Anthony
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