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Hello all,I hope everyone is staying safe and sane out there. I'm running an analysis looking at brain structure differences associated with two separate environmental exposures in children (both dichotomous variables). I am controlling for sex and age, and I'm not interested in interaction effects. My go-to strategy does not match the documentation, but I'm not sure the documented solution using fsgd files will meet my needs. I was hoping I could check with the list about best practices.
Because some participants have both exposures, my intuition was to use dummy coded variables so that the design matrix looks like this:
+1.00000 +3.11124 -0.48322 -0.51007 -0.53020
+1.00000 -0.20820 +0.51678 -0.51007 -0.53020
+1.00000 -2.06654 -0.48322 -0.51007 -0.53020
...
Where the columns are (mean, mean centered age, mean centered sex, mean centered exposure 1, mean centered exposure 2). A contrast for exposure 1 - exposure 2 would then look like this:
[0, 0, 0, 1, -1]
However, it's clear from the documentation that, given 3, 2 level factors, the recommended approach is to specify 8 classes (one for every combination of factors) and the covariate in an fsgd file. I can remove the interaction terms from the resulting matrix, but even so it looks very different than what I had above:
+1.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000 +3.11124
+0.00000 +1.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000
+1.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000 +0.00000 -2.06654
...
With a contrast with all classes with exposure 1 set to '1', and all classes with exposure 2 set to '-1' - with the order of the classes in the fsgd file it comes out as [-1 -1 1 1 1 1 -1 -1 0].
I'm not positive that this is the right way to model differences associated with each exposure given unequal #s of participants with neither/both/just one exposure, but I certainly don't have the expertise to be certain. Could someone give me some guidance on the best approach here? Thank you,
-Matt
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