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Tom,

Thanks for the reply. Indeed it is a tricky thing when the data are
unbalanced, but I'll give it a try using only year 1 and 2 data.

On Thu, Apr 17, 2008 at 6:24 AM, Thomas Nichols <[log in to unmask]> wrote:
> Dear Robert,
>
> Randomise only fits OLS GLM's, i.e. unweighted least squares fits.  That
> said, randomise can accommodate certain types of repeated measures data, the
> simplest type of which is paired data.  When you have just two time points,
> follow the instructions for paired data analysis.  When you have three or
> more observations per subject, things get slightly trickier.
>
> If you have k>=3 repeated measures, you could simply treat the data like a
> 2-way ANOVA, modeling one longitudinal factor, and one subject factor, but
> since you're not doing a proper mixed effects model, the t-statistics won't
> really be what you want (i.e. they won't be capturing the correct between
> subject variation in the denominator).  For the paired case, k=2, it happens
> to works out exactly correct (i.e. OLS & MFX are the same), and the k=3 case
> probably isn't too far off (if an assumption of compound symmetric
> correlation--i.e. all equal correlations--within subject is true, and the
> design is balanced, randomise's OLS results should match a full-blown mixed
> effects model).  But for imbalanced design (k varying between subject) and k
> of 4 or more, you probably shouldn't trust OLS to be giving you sensible
> answers.
>
> The safest approach for k>3 would be to create summary measures for each
> subject, and then anlayze those with a simple second level model.  For
> example, if you're interested in slope, or change over time, fit a simple
> linear regression model for each subject, and then model the images of slope
> coefficients.
>
> Ideally there would be some scripts cobbled together to aid with such
> longitudinal analyses, but does this give you an idea of what randomise can
> and can't do?
>
> -Tom
>
>
>
>
>
> On Wed, Apr 16, 2008 at 3:50 AM, Robert Terwilliger <[log in to unmask]>
> wrote:
> > Dear FSL,
> >
> > We have been doing DTI analysis using TBSS successfully for some time
> > now. Our cohort consists of normally developing adolescents.
> >
> > As a small example of our analysis consider a sample of FA images from
> > six subjects (we have many more, but this is for simplicity), ages
> > 12-17. I do a "within group" design in which the log of age is the
> > regressor. The resulting design.mat and design.con files are as
> > follows:
> >
> > **************
> > design.mat
> > **************
> > /NumWaves 1
> > /NumPoints 6
> > /PPheights 1
> > /Matrix
> > -0.145
> > -0.145
> > -0.065
> > 0.009
> > 0.143
> > 0.203
> >
> > *************
> > design.con
> > *************
> > /NumWaves 1
> > /NumContrasts 1
> > /PPheights 1
> > /Matrix
> > 1
> > -1
> >
> > The values in design.mat are the demeaned natural log of the subjects'
> ages.
> >
> > So far, so good.
> >
> > Now fast forward a couple of years....This is actually a longitudinal
> > study, with each subject scanned on an annual basis. As is common in
> > longitudinal studies, not every subject is scanned every year, for a
> > variety of reasons.
> >
> > If we consider only the first year scans as we are doing currently,
> > each subject can be treated as an independent sample. However, now
> > some subjects have had three scans, some have two, and a few didn't
> > make it past the first year.
> >
> > Is there a way to set up a model in randomise where we can include
> > multiple DTI scans from the same subject? This would violate the
> > assumption of independence in the simple model, but we're looking for
> > a way to account for the within-subject correlation in a mixed-model
> > design.
> >
> > Many thanks,
> >
> > Robert Terwilliger
> > Laboratory of Neurocognitive Development
> > University of Pittsburgh
> >
> >
>
>
>
> --
> ____________________________________________
> Thomas Nichols, PhD
> Director, Modelling & Genetics
> GlaxoSmithKline Clinical Imaging Centre
>
> Senior Research Fellow
>  Oxford University FMRIB Centre