Correcting for type 1 error in mlwin

[SibelHalfon]

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 because it is too conservative for my data.

Many thanks

[SibelHalfon]

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.

[billb]

I would imagine if you don't want to do Bonferroni then you could try something like Benjamini & Hockberg's FDR which is less conservative (false discovery rate) and I believe could be used for most models. Don't think there is an estimation procedure related approach like tweaking IGLS here.

[SibelHalfon]

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?

[billb]

Not for fixing the issue of multiple comparisons as bootstrapping will not help with that.