Hi,
This is really one for Tom, yes you're right that treating this as a
two-group t-test doesn't give you much p-value resolution. Unless Tom
has any brilliant ideas I would think that you probably just need to
summarise the control group with a mean and variance and use
parametric statistics to get a p-value on the individual patients
(e.g. use fsl_glm).
Cheers, Steve.
On 22 Nov 2007, at 10:43, Dr Niels Focke wrote:
> From a clinicians point of view a comparison of individual patients
> against
> a group of controls can be very useful. However it is a violation of
> sphericity. I am wondering how randomise (using tbss-data) deals
> with this
> scenario? In theory it should be even more tolerant than a GLM. Do
> you think
> it is statistically valid to use such an approach with a permutation-
> based
> inference?
>
> Interestingly when I run randomise on this data it will only perform 1
> permutation per case (e.g. I had 35 controls and 1 patient it would
> prompt
> that 36 permutations are exhaustive and only do 36 regardless of
> what I
> specify with the -n option). Of course on the positive side this is
> very
> quick (~ 3-5 minutes) but again I am wondering if I can trust the
> results...
>
> Additionally is it necessary to demean tbss data with randomise? In
> the tbss
> documentation the -D flag is not set. However in the randomise
> documentation
> the -D option is recommended.
>
> Any comments from the experts?
>
> Thank you very much
>
>
> Niels
>
> Research Fellow
> ION
>
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
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