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Thank you so much.

Would you suggest bootstrapping in this case? I know it does not directly address type 1 error but can provide more accurate estimates and confidence intervals?

Just to explain my data I have three levels where outcome measures are nested in patients who are further nested in clinicians. The measures are collected repeatedly over 5 time points. I am interested in assessing whether there is a differential change in outcome based on treatment type.

I have multiple outcome measures from the same patients and have to run multiple simultaneous analyses on the same data. Is there some way I can correct for type 1 error (eg using a different estimator than igls) or some other way to adjust for this? I don't want to do a Bonferroni correction ...