Which test to use for binary variables and concerns about multiple comparisons

#1
Hi all,

I am working on a research project attempting to compare two groups of subjects' (call them A and B) brains based on MRI results.

Let's say in my data set I have split each subject's brain into 50 regions, and then classified each region as either "healthy" or "not healthy" i.e. assigned a 1 or 0 as a binary variable.

If I compare the total number of "not healthy" brain regions for each subject across these two groups, I see they are different (p<.05 using either two-sample t-test or a non-parametric rank-sum test) and would like to explore if a subset of particular regions is driving this group difference. Let's say Group A tends to have fewer total "healthy" regions than Group B.

Is the appropriate test now to do a two sample chi-square test for each region? If so, wouldn't I need to correct my p-values for multiple comparisons using either Bonferroni or FDR and need a p<.001 (i.e. 0.05 / 50)?

Is it legitimate to only look at the regions with lower average healthy % in group A and do a multiple comparisons correction across that number (which should be less than 50 presumably)?

Thanks in advance,
-mnd12
 

hlsmith

Not a robit
#2
Why not primary outcome two sample chisq, uncorrected (proportions healthy).

Then secondary outcomes, 50 regions, using two sample chisq, yes alpha level corrected.
 
#3
Why not primary outcome two sample chisq, uncorrected (proportions healthy).

Then secondary outcomes, 50 regions, using two sample chisq, yes alpha level corrected.
Thanks for your response!

For the primary outcome, I have a proportion for each subject, say 20 in both group A and B--how do I use these 40 proportions into a 2 sample chi-square test? I thought a two sample chi-square was for comparing 2 proportions?

Thanks
mnd12