Hi Rowena,

Please see below:


On 17 August 2015 at 16:04, Rowena <[log in to unmask]> wrote:
Dear Anderson,

Thanks for your reply and I apologize for my late response, I must have missed out the email notification.

One clarification: In your response to demeaning, you mentioned "If you won't test the effect of these nuisance variables, but just compare groups, demeaning isn't needed. Only if you'd like to test the effect of age and sex, or test the mean for each group (which surely you won't for TBSS)."

If I were to wish to test the effect of age and sex in a manner similar to that of what is mentioned in the FSL GLM Guide (section on 'Two-Group Difference Adjusted for Covariate'), but with 3 groups (Patient Group A, Patient Group B, Controls), would I also follow that same procedure listed in the guide such that:

For contrasts -

EV1 (PT_A) EV2 (PT_B) EV3 (CON) EV4 (AGE) EV5 (GEN)
0                 1               -1              0              0
0                -1                1              0              0
1                -1                0              0              0
-1                1                0              0              0
1                 0                -1             0              0
-1                0                1              0              0
0                 0                0               1              0
0                 0                0               -1             0
0                 0                0               0              1
0                 0                0               0              -1


I am not sure if this is the right procedure and would appreciate feedback.

Yes, this is fine.

 

Following this, the guide mentions about a 'positive and negative age effect' which if I may clarify, refers to whether age as a covariate has a positive or negative effect on the DV?

Yes, exactly.
 

Finally, I would like to ask if I went along with such a model as stated above, would it be necessary to always consider and test for interaction effects, or would that be highly dependent on one's hypothesis? In this case, I am not particuarly interested in whether gender x age would affect FA values, but I am not sure if statistically, it would be necessary to include.



If you aren't interested, and if there's no reason to suspect that the interaction could be a nuisance, then no need to include interaction effects in the model.

All the best,

Anderson

 
Thanks very much!

Best,
Rowena