Hi,
I have done an analysis involving three groups, so there are three pairwise comparisons across two hemispheres = 6 p-maps. I want to adjust for multiple comparisons (across the vertices), so I use FDR. But since FDR determines the threshold basd on the actual p-values, I get 6 different tresholds:

 

comparison 1: lh and rh,  0.016 and 0.028 (I can choose .01)
comparison 2: lh and rh,  0.01 and 0.001 (I can choose.001)
comparison 3  lh and rh,  0.001 and 0.0001 (I can choose .0001)

 

There are lots of significant vertices in comparison 1 and nothing significant, after correction, in comparison 3. Is there anything wrong with using different tresholds here, and concluding that in comparison 1 there were extensive differences between the groups, whereas in comparison 3 there were none? I'm not sure if this is a problem, but I'm afraid some reviewers might have an issue with it. Across the hemispheres, I can choose a conservative threshold which covers both hemispheres, i.e. lower than both the FDR-adjusted treshold for lh and rh. But between the comparisons the tresholds differ even more, by a factor of 10 and 100. And if I choose the most conservative of all the adjusted thresholds, I'm afraid that I'll make a type II error in comparison 1.

 

From what I understand, the adjusted threshold for comparison 3 is more conservative because of the actual empirical data (the distribution of p-values), so that's an empirical argument for using a more conservative threshold there.

And: What if I pooled all thre p-maps (sig.mgh) and did an FDR on the whole thing, would that be a better approach? And does Freesurfer use the Benjamini algorithm, and if you do, can I use Tom Nichols' matlab function for FDR (http://www.sph.umich.edu/~nichols/FDR/FDR.m) for pooling all three p-maps?
Thank you!

--
yours,
LMR


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