Hello everyone,

I want to run a post - treatment analysis of my longitudinal data. We have already done the analyses (using MIXED) of baseline (T1), post-treatment (T2) and six-month followup (T3). Now, we are interested in T2 (post-treatment) versus T4 (12-month followup). I want to use multiple imputation for T2 missing data (subject dropout) in order to maintain ITT approach. I want to use 2x2 mixed (1 within-, 1 between-subject factor with 2 levels each) ANOVA to compare two treatments on how they change between T2 and T4.

I know I can accomplish it by first doing Multiple Imputation in SPSS and then running MIXED analysis equivalent to such an ANOVA (I think SPSS does not support GLM ANOVAS for multiply imputed data sets). But here is my conundrum:

My assumption #1 is that I should include baseline variables in the multiple imputation procedure in order to maximize the quality of imputed T2 data.

My assumption #2 is that I should first create the long format file, then do Multiple Imputation, and then run the MIXED syntax on the imputed files.

If my assumptions 1 and 2 are correct, how do I include baseline in the long file for the imputation but then ignore it for the MIXED procedure run on the imputed files? I will need a time index and I will have three rows of data for each subject instead of two...

Are my assumptions correct? If yes, what can I do?

thanks so much in advance!

bozena

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