Hi Angela,
let's see if I got it right.
1) Besides testing for slopes, I am also interested in average group
differences. Thus, if ages weren't matched, I would be introducing a
confound, i.e., any effect introduced by progressing age (e.g. task
speed) would also influence the group mean. As long as I demean ACROSS
groups, this will not influence the *group means* and their contrasts
- in the GLM, any *shared* variability simply disappears (and will
lower "sensitivity" of either contrast, and rightly so. Teaches me to
match groups the next time, as Jesper put it two days ago).
If, on the other hand, I demeaned only within groups, I would not
correct for the fact that there was a significant contribution of the
factor "age" to either group. All variability due to the difference of
age means would be soaked up by the group means and their contrasts.
Therefore, if these group contrasts showed something significant, it
might have been just due to the age difference (group a is slower than
b, but also happens to be the older one!), but not due to treatment or
diagnosis, or whatever I was actually interested in.
2) As far as I got it, if you are only interested in correlations with
a (demeaned) covariate and did not model any group mean, you also
should demean the data before "randomise"ing. As an example: running
randomise on VBM data of a depressed patient cohort, looking for GM
changes correlating with a suicidal ideation score ranging from -5 to
+5, mean 0. In this case, randomise -D will do the demeaning of the
DATA (not the covariates) for you, saving you the effort of running
fslmaths on the data.
If anything of this is wrong, I am sure one of the other contributors
will point it out rather quickly and I'll have lost posting rights for
4 weeks or so :-)
Cheers,
Cornelius
On Thu, Mar 31, 2011 at 11:36 PM, Angela Favaro <[log in to unmask]> wrote:
> Dear FSL Masters,
> this discussion has been very helpful for me.
> But I still have two doubts:
> 1. Demeaning within groups is more an exception than a rule, but it is the
> correct thing when I want to test differences between slopes (and not
> differences between groups). Is it correct?
> In the example below the two groups have a similar age. What happens if
> the covariate differs in the two groups?
>
> 2. What continues to be unclear to me is the use of the -D option in
> randomise. When is it necessary/advisable to use it? Only in one group
> covariate analysis?
>
> Thank you
>
> Angela
>
>
>
>> Yeah, that's what I thought. And basically that's why I asked in the
>> first place :-)
>> But thanks for all the contributions to this topic. I believe I have
>> an idea on how to go about it, now.
>> Best regards,
>> Cornelius
>>
>> On Thu, Mar 31, 2011 at 5:06 PM, Michael Harms <[log in to unmask]>
>> wrote:
>>> Just wanted to chime in that demeaning the performance EV separately
>>> within group is a rather unique case that is specific to this particular
>>> post.
>>>
>>> Recent posts by Jesper (just yesterday), Jeannette, Tom, and myself have
>>> all advised that, in general, one should demean across all subjects (NOT
>>> within group separately).
>>>
>>> Given the recent posts on this, I thought it was worth making explicit
>>> that demeaning within groups is not a "typical" situation.
>>>
>>> And as a matter of good reporting practice, any time that demeaning is
>>> performed separately within group, rather than across all subjects, that
>>> should be noted (and justified) very explicitly in any presentation of
>>> the ensuing results.
>>>
>>> cheers,
>>> -MH
>>>
>>> On Thu, 2011-03-31 at 08:42 +0100, Stephen Smith wrote:
>>>> Hi
>>>>
>>>> On 30 Mar 2011, at 11:30, Cornelius Werner wrote:
>>>>
>>>> > Hi,
>>>> >
>>>> > sorry to revive such a well-worn topic. But there is something I did
>>>> > not quite get so far.
>>>> > As an example, I am examining a patient cohort and a control cohort
>>>> > in
>>>> > a Dual Regression setup (resting state data). Patients and controls
>>>> > are matched for age and gender. They obviously differ in diagnosis,
>>>> > but also in one performance score. I am interested in basic group
>>>> > differences and the differential correlation of connectivity
>>>> > strength
>>>> > of several RSNs with performance. For the final randomise-step, my
>>>> > design matrix has a column for group mean "patient" and one for
>>>> > "controls" (consisting of 1, padded with zeroes where applicable),
>>>> > and
>>>> > two separate columns for age (as a confounder) - one for each group,
>>>> > respectively, because an age*group interaction on connectivities
>>>> > could
>>>> > not be excluded a priori. As I was modelling the group mean
>>>> > separately, only the slopes associated with age were tested. Is that
>>>> > correct so far?
>>>>
>>>>
>>>> I think so - sounds fine.
>>>>
>>>> > As the age means did not differ (tested beforehand),
>>>> > does it matter if I demeaned within group or across groups?
>>>> > Shouldn't
>>>> > the intercept be modelled by the group mean regressor, in any case?
>>>> > Following Tom's last post, I'd probably demean across groups.
>>>> >
>>>> > The next thing is even more unclear to me:
>>>> > Due to an expected group*performance interaction (i.e. steeper slope
>>>> > of increases in connectivity along with better performance in
>>>> > contrast
>>>> > to the other group), also the performance scores are split. BUT:
>>>> > should I demean?
>>>>
>>>>
>>>> Yes - if you want to compare the *slopes* between the two groups,
>>>> demean the performance scores within group before padding with zeros,
>>>> for each group's performance EV.
>>>>
>>>> > And if so, within groups, or across groups? In this
>>>> > case, mean differences in performance are believed to be *due to*
>>>> > diagnosis - therefore, variability associated with the mean should
>>>> > go
>>>> > to the group regressor, shouldn't it? In this case, I'd be inclined
>>>> > to
>>>> > demean in order not to affect the group mean regressor negatively,
>>>> > and
>>>> > to demean within groups, because of the (clearly) attributable mean
>>>> > variability...?!
>>>> >
>>>> > Example:
>>>> >
>>>> > EV1: Patient mean
>>>> > EV2: Control mean
>>>> > EV3: Patient age (demeaned across groups - EV of no interest)
>>>> >
>>>>
>>>>
>>>> I presume you mean demeaned within group, then padded with zeros.
>>>>
>>>>
>>>> Cheers.
>>>>
>>>> > EV4: Control age ( " )
>>>> > EV5: Patient performance score (demeaned within patients)
>>>> > EV6: Control performance score (demeaned within controls)
>>>> >
>>>> > Patients>controls: 1 -1 0 0 0 0
>>>> > Controls>patients: -1 1 0 0 0 0
>>>> > Slope(performance score) patients > Slope(performance score)
>>>> > controls:
>>>> > 0 0 0 0 1 -1
>>>> > Slope(performance score) controls > Slope(performance score)
>>>> > patients:
>>>> > 0 0 0 0 -1 1
>>>> >
>>>> > Please don't hit me - I'm having a hard time getting my head around
>>>> > this :-)
>>>> > Cheers,
>>>> > Cornelius
>>>> >
>>>> >
>>>>
>>>> ---------------------------------------------------------------------------
>>>> 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)
>>>> [log in to unmask] http://www.fmrib.ox.ac.uk/~steve
>>>> ---------------------------------------------------------------------------
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>
>>
>>
>> --
>> Dr. med. Cornelius J. Werner
>> Department of Neurology
>> RWTH Aachen University
>> Pauwelsstr. 30
>> 52074 Aachen
>> Germany
>>
>>
>
--
Dr. med. Cornelius J. Werner
Department of Neurology
RWTH Aachen University
Pauwelsstr. 30
52074 Aachen
Germany
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