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Dear all,

After some days of reading, I've decided that I would like to see if MI will provide different results for my data. Assuming that I want to impute response variables (repeated measures), is the approach I pointed out above OK?

It took me some minutes only: Journal of Clinical Epidemiology 66 (2013) 1022e1028
Multiple imputation of missing values was not necessary before
performing a longitudinal mixed-model analysis
Jos Twisk a,b, *, Michiel de Boer b , Wieke de Vente c , Martijn Heymans a,b

Thanks, Dr. Goldstein.

So, apparently there is no point in imputing responses in that repeated measure analysis? No gain in terms of standard errors?

Tiago

Dear all,

My question is how to organize data in order to impute a two-level model with 5 repeated measures.

To sum up: We have been using Stata and RealComImpute. Level 1 = patient id, Level 2 = month.

Month is a repeated measure taken from the same patient at month 0 (baseline), 1, ..., 4 ...