Dear Allstat,
I have a longitudinal dataset from a 2 treatment group clinical trial with
baseline and 4 follow up timepoints. I may have missing baseline covariates
and follow up data.
As I am interested in the average effect of the treatment groups I wish to
analyse using a GEE model. As the missing data is likely to be Missing At
Random (MAR) I have read that GEE models are likely to provide biased
estimates.
My thoughts were to multiply impute the missing data and then carry out a
GEE model.
I would be interested to hear your views on whether this is the best
approach (if so how best to impute the missing data?) , or are there
alternative approaches.
Best Wishes
Clare Rutterford
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