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On Wed, May 23, 2012 at 4:29 AM, Ahnate Lim <[log in to unmask]> wrote:

> Hello,
>
> Thank you for your reply.
>
> I just came across this tutorial:
> http://www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/feat2/index.htm
> and am thinking of going the FEAT route if possible, and using randomise
> only if necessary. Using your suggestion, would the following analyses be
> appropriate?
>
> 1) For the between-subjects effect of HIV status, use the 3 level approach
> of:
>  a) Lower level FEAT for each scan for each subject
>  b) Between-session analysis creating a mean response for each subject
> (averaging drug & placebo)
>  c) Between-subject analysis for HIV effect (fixed-effects)
>

Yes. This would work.


>
> 2) For the within-subjects effect of drug, use the paired t-test approach
> (FLAME1). HOWEVER, can this be modified to model the interaction of drug &
> HIV? The interaction effect is crucial, and I'm the most unclear about how
> to model this. Is the following correct?
>
>                      EV1     EV2     EV3     EV4     EV5     EV6     EV7
>   EV8
> Control1_placebo       0       1       1       0       1       0       0
>     0
> Control2_placebo       0       1       1       0       0       1       0
>     0
> HIV1_placebo           0       1       0       1       0       0       1
>     0
> HIV2_placebo           0       1       0       1       0       0       0
>     1
> Control1_drug          1       0       0       1       1       0       0
>     0
> Control2_drug          1       0       0       1       0       1       0
>     0
> HIV1_drug              1       0       1       0       0       0       1
>     0
> HIV2_drug              1       0       1       0       0       0       0
>     1
>

EV3 and EV4 should code the group.
1 0
1 0
0 1
0 1
1 0
1 0
0 1
0 1

You should insert 4 columns before EV5 that represent the interactions:
1 0 0 0
1 0 0 0
0 1 0 0
0 1 0 0
0 0 1 0
0 0 1 0
0 0 0 1
0 0 0 1

The contrasts should be:

                  EV1   EV2   EV3   EV4   EV5   EV6   EV7   EV8 ... EV12
C1 drug-placebo    1     -1     0     0     0     0     0     0...
C2 placebo-drug    -1     1     0     0     0     0     0     0...
C2 HIV*drug_1(placebo)      0     0     0     0     0     1     0     0...
C2 HIV*drug_2(drug)      0     0     0     0     0     0     0     1...

>
> This example is a simplification, as we currently have unequal group
> sizes, but would this approach to modeling the interaction effect be
> correct?
>

I'd have to check to see if my model was equivalent to your model. I'd
model each group*condition with a separate column though - rather than
collapsing them as you have done. It'll give you more flexibility.

>
> Thank you,
> Ahnate
>
>