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I suppose that the additional covariates should be demeaned in either approach 2014-01-27 Anders Hougaard <[log in to unmask]>: > Dear all, > > I realize that this post is actually off-topic as it really concerns > (very basic) general statistics and not FSL specifically, I just > happen to be using FSL for the analysis. My apologies for this. > > I am comparing fMRI results from a group of subjects with a condition > that may or may not affect the results, and a group of individually > age- sex matched control subjects. > > I would like to test if the presence of the condition affects the fMRI > results, and if/how this effect depends on the severity of the > condition. > The condition is expected to be associated with subtle changes of the > results only. > I cannot rule out, that other subject-specific parameters, such as > BMI, cerebral volume or IQ, could affect the results. > I would like to do as sensitive an analysis as possible. > > Would it be OK to apply a paired/longitudinal design for this purpose? > > Consider the two groups of subjects: > A - subjects with condition > B - matched subjects without condition > > Group A > subj scan_results severity age gender > 1 X1 3 29 2 > 2 X2 5 25 1 > 3 X3 4 33 2 > 4 X4 7 28 2 > > where, in the gender column, 2 is female, 1 is male, and > > Group B > subj scan_results severity age gender > 1 Y1 0 29 2 > 2 Y2 0 25 1 > 3 Y3 0 33 2 > 4 Y4 0 28 2 > > I imagine doing testing the differences between groups in a paired fashion: > > Differences > scan_diff severity_diff age_diff gender_diff > X1-Y1 3 0 0 > X2-Y2 5 0 0 > X3-Y3 4 0 0 > X4-Y4 7 0 0 > > To do this, I would use a GLM like this: > > scan_diff = mean + severity_diff > > Could I even control for the (small) age differences by adding this as > another confound: > > scan_diff = mean + severity_diff + age_diff ? > > Another approach would be to do an unpaired design, in which the > dependent variable would be a column of scan results from all > subjects: > > scan_results severity age gender > X1 3 29 2 > X2 5 25 1 > X3 4 33 2 > X4 7 28 2 > Y1 0 29 2 > Y2 0 25 1 > Y3 0 33 2 > Y4 0 28 2 > > And to correct for differences due to age and gender, the model would be: > > scan_results = mean + severity + age + gender > > What are the principal differences between these two approaches and > which one would you consider most appropriate and most sensitive for > this purpose? > > Best, > Anders