Thanks for your reply,

I have a follow up on my first question - if however I was not looking for a main effect of gender, but for the main effect of something else (e.g. diagnosis), will it then be ok to include the unbalanced group (i.e. A-male), to control for gender?

Thanks!

Regards
Yann

On 7 July 2017 at 18:34, <freesurfer-request@nmr.mgh.harvard.edu> wrote:
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On 7/7/17 1:58 PM, Yann Ying Chye wrote:
> Dear FreeSurfer mailing list,
>
> I've got two questions regarding the freesurfer GLM analysis.
>
> 1. I am trying to run mri_glmfit on the cortical thickness of a
> dataset that has a few interacting factors of unequal levels.
> For example, my dataset may have groups A, B, C, and D. Within the
> groups, subjects are further divided by gender - male and female.
> However, group A only has males, such that the classes in my FSGD file
> = A-male, B-male, B-female, C-male, C-female, D-male, and D-female.
> Will this design be a problem? Will it be statistically sound?
> Additionally if I was looking for an effect of gender, how would I
> weigh my contrast? I was thinking 0.25 0.25 -0.33 0.25 -0.33 0.25
> -0.33. Would that be valid?
I think I would lean against including A-male. Otherwise, what is the
null hypothesis? Without A-male, it would be that males and females
differ when regeressing out the effect of group, but you can't say that
if you include A-male. Eg, if there were no difference between A-male
and A-female but A tended to be larger than the other groups, then
including A-male and not A-female would create a confound making males
look bigger than females.
> 2. Does mri_glmfit have a limit to the number of subjects it can run
> with? If I have say upwards of a few thousand subjects, will this be
> possible to run/how long would that take?
I don't think it has a limit.
>
> Thanks for your help!
>
> Regards
> Yann
>
>
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End of Freesurfer Digest, Vol 161, Issue 17
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