>
> With SPM99's ability to get corrected p-values for subvolumes, I don't
> see the need to muck about with uncorrected thresholds.
>
> Do a pilot study, generate some hypotheses, specifically, generate a
> mask image that defines your a priori region of interest; then get
> corrected p-values for your subvolume... and your done. You've gained
> power by focusing on a smaller region yet you're still controlling for
> the multiple comparions within that region.
>
>
> > One drawback I could see with using a fixed-effects pilot study followed
> > up by a random-effects study is that the fixed-effects subjects could
have
> > been allocated to the random-effects study in the first place, which
would
> > increase the power of the random-effects analysis. It's unclear whether
it
> > is better to just increase the N for the random-effects analysis or do a
> > two-stage approach with a fixed-effects hypothesis-generating stage
> > followed by a random-effects analysis with an independent group -- any
> > ideas?
>
> This is a good point; I'm afraid I don't have a pat answer. The
> issues include how much power is gained by the localization obtained
> from the pilot study versus how much is lost from the final
> random-effects study, also the size of the region will play a
> role. (The smaller the region, the greater the power you can gain from
> just focusing on that area.)
Hi Tom,
I'm not sure I agree with this approach. A complimentary issue that is not
often discussed is a Type II error. I don't believe that we know enough
about brain systems to make good a priori hypotheses. Doing a pilot
study to generate a mask still doesn't get away from the problem that
one, or a few, subjects may drive the fixed analysis, and hence the
subvolume
mask.
If you're going to do 10 subjects for a pilot study, it's not really that
much
more difficult to do 20 or 30, and then do a random effects analysis. In
our
random effects designs, we don't see stability of the results until about
N=20.
Greg
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