If you want to find the effect of group regressing out the effect of age 
and there is truly a difference in the age slopes, then you have a 
problem. Imagine a scatter plot of thickness vs age. Each group gets 
it's own best fit line (offset and slope). If the slopes are the same, 
then the two lines will always have the same distance between them.  
However, if the slopes are different, then the difference between the 
groups depends on age. When you "regress out age", what you are doing is 
selecting a single age to test the groups. If you don't demean, then you 
test at age=0. If you demean, you test at age=mean age. The problem is 
that you get different results depending upon what age you test at. 
There will be an age where the two lines intersect at which point there 
will be no difference. Before that age, one group will be greater than 
the other. After that, it will reverse. Some statisticians say that you 
should not test for a difference between groups if the slopes are 
different (ie, your analysis has come to an end). Most people just 
demean and plow ahead without understanding what it means. If you really 
think that there is a difference in the slopes despite the negative test 
result, then you can abandon the analysis or proceed with the 
understanding of the problems with interpretation. I would probably 
trust the negative result and proceed with the DOSS test. So, ...


1) Yes

2) Publish

3) Probably not. Adding the males gives you a net increase of 17 DOF, 
which is probably not trivial


On 11/30/2016 09:45 PM, Rodrigo Gonzalez Huerta wrote:
>
> Hey Doug,
>
>
> Sorry for keep bothering, today I was trying to do what you told me 
> yesterday, but some doubts arose. I ran QDEC using DODS to see if 
> there were any interactions (significant regions) between age (age 
> variable was not demeaned) and group. I didn’t found anything, no 
> significant regions survived monte carlo. If that came to happen, you 
> suggested me to use DOSS instead, but… because of the characteristics 
> of the disease I’m studying, the slopes shouldn’t be similar between 
> patients and controls. My questions are:
>
>
> 1) Can I proceed using DOSS anyway?
>
>
> 2) What if DOSS give me some clusters after running monte carlo? Which 
> is the case since I’ve already ran mri_glmfit and obtained three 
> clusters. What should I do now? Should I re-run DODS with age-variable 
> demeaned?
>
>
> 3) Since most of my subjects are women (around 80%) and my sample is 
> small (18 patients, 18 controls), does it have an advantage to remove 
> the males to be able to get rid of the gender variable that I’m also 
> regressing out? In order to have just two classes (patient and 
> control) instead of four (malecontrol, malepatient,….) , or it is 
> better to continue as I’ve been doing, controlling for the effect of 
> gender.
>
>
> Thanks for your help!
>
>
> Rodrigo
>
>
> ------------------------------------------------------------------------
> *De:* freesurfer-boun...@nmr.mgh.harvard.edu 
> <freesurfer-boun...@nmr.mgh.harvard.edu> en nombre de Douglas Greve 
> <gr...@nmr.mgh.harvard.edu>
> *Enviado:* martes, 29 de noviembre de 2016 08:31:20 p. m.
> *Para:* freesurfer@nmr.mgh.harvard.edu
> *Asunto:* Re: [Freesurfer] Demeaning variables
>
>
>
> On 11/29/16 8:52 PM, Rodrigo Gonzalez Huerta wrote:
>>
>> It makes sense to me, I only have two more questions,
>>
>>  1. When you say “see if there are any places where there is an
>>     interaction between age and group”, do you mean finding
>>     significant statistical regions, right? And this would be before
>>     or after using monte carlo?
>>
> After. Even if you have some sig areas, they won't matter if they do 
> not overlap with sig areas in DOSS
>>
>> 1.
>>
>>
>>  2. What about using the median or other value instead of the mean
>>     since most of my subjects are in the range from 20 to 35 years
>>     old and only a few in the range from 35 to 55 years old. Would
>>     this make sense to you?
>>
> It has nothing to do with using the mean or median. The problem is 
> that the differences in the means of the groups is not a valid concept 
> when the age slopes are different
>>
>> 1.
>>
>>
>> Thanks in advance
>>
>> Rodrigo
>>
>>
>> ------------------------------------------------------------------------
>> *De:* freesurfer-boun...@nmr.mgh.harvard.edu 
>> <freesurfer-boun...@nmr.mgh.harvard.edu> en nombre de Douglas Greve 
>> <gr...@nmr.mgh.harvard.edu>
>> *Enviado:* martes, 29 de noviembre de 2016 07:33:47 p. m.
>> *Para:* freesurfer@nmr.mgh.harvard.edu
>> *Asunto:* Re: [Freesurfer] Demeaning variables
>>
>> It comes down to the contrast you care about. If you want to look at 
>> the difference in mean thickness between the groups regressing out 
>> the effect of age, then demeaning can make a big difference. If this 
>> is what you want, then you need to do the analysis in two stages. 
>> First run with DODS and see if there are any places where there is an 
>> interaction between age and group; demeaning is unimportant for this 
>> contrast. If there is not a difference, then reanalyze using DOSS (in 
>> which case demeaning is also unimportant). You cannot do DOSS in QDEC.
>>
>>
>>
>> On 11/29/16 6:23 PM, Rodrigo Gonzalez Huerta wrote:
>>>
>>>  Hi Doug,
>>>
>>>
>>> I mean that when I run QDEC selecting gender and diagnosis as fixed 
>>> factors, age as nuisance factor, and display the results, I obtain 
>>> totally different significant statistical regions (different 
>>> statistical maps) when ages are demeaned ((demeaned age = age of 
>>> each subject – the mean of all subjects (grandmean)) than the case 
>>> when ages aren't demeaned. Therefore, when I run the monte carlo 
>>> simulation, when I demean the age variable I don’t get any clusters, 
>>> but when age is not demeaned I obtain some clusters of considerable 
>>> size. The contrast I used in mri_glmfit is:
>>>
>>> 0.5 -0.5 0.5 -0.5 0 0 0 0 ("is there a difference between patient 
>>> and control regressing out the effects of gender and age?")
>>>
>>> In QDEC it seems to correspond to the output 'Does the average 
>>> thickness accounting for gender differ between patient and control? 
>>> (age as nuisance variable)’ because the statistical maps look the same.
>>>
>>> From what I understand, this difference in the results when age 
>>> variable is demeaned is something I should expect when doing a DODS 
>>> analysis. My main doubt would be if it was necessary to demean the 
>>> age variable and enter it as a nuisance factor based on the 
>>> characteristics of my population and the analysis I want to perform. 
>>> I tried to focus my questions in that direction. I’m very sorry if I 
>>> am not giving myself to understand. Thanks!
>>>
>>>
>>> Rodrigo
>>>
>>>
>>> ------------------------------------------------------------------------
>>> *De:* freesurfer-boun...@nmr.mgh.harvard.edu 
>>> <freesurfer-boun...@nmr.mgh.harvard.edu> en nombre de Douglas N 
>>> Greve <gr...@nmr.mgh.harvard.edu>
>>> *Enviado:* martes, 29 de noviembre de 2016 03:22:15 p. m.
>>> *Para:* freesurfer@nmr.mgh.harvard.edu
>>> *Asunto:* Re: [Freesurfer] Demeaning variables
>>> When you say that the results are different, what do you mean? What is
>>> your contrast? What is your command line? What is your fsgd file?
>>>
>>>
>>> On 11/29/2016 03:34 PM, Rodrigo Gonzalez Huerta wrote:
>>> >
>>> > Just to clarify, where is said N=18 I wanted to meant 18 controls 
>>> + 18
>>> > patients. Thanks in advance.
>>> >
>>> >
>>> > Rodrigo
>>> >
>>> > 
>>> ------------------------------------------------------------------------
>>> > *De:* freesurfer-boun...@nmr.mgh.harvard.edu
>>> > <freesurfer-boun...@nmr.mgh.harvard.edu> en nombre de Rodrigo 
>>> Gonzalez
>>> > Huerta <rghbecker2...@hotmail.com>
>>> > *Enviado:* martes, 29 de noviembre de 2016 02:19:26 p. m.
>>> > *Para:* freesurfer@nmr.mgh.harvard.edu
>>> > *Asunto:* [Freesurfer] Demeaning variables
>>> >
>>> > Hi Freesurfers,
>>> >
>>> >
>>> > For obtaining my undergraduate degree I’m performing a study using
>>> > Freesurfer. I’m stuck with the centering (de-meaning) issue. I have
>>> > two discrete variables, gender, and patient-control, and a continuous
>>> > variable which is age. The age population range goes from 20 to 55
>>> > years and is paired between patients and controls. Patient’s disease
>>> > affects cortical thickness over time and every patient has been
>>> > presenting the disease for 6 years (no more, no less). I’ve runned
>>> > QDEC and mri_glmfit using DODS obtaining the same results, the 
>>> problem
>>> > is that when I demean the age (I used the grandmean) the results are
>>> > very differente. If I don’t demean some clusters survive the
>>> > monte-carlo multiple comparisons correction (p<0.05), but, when I
>>> > demean no cluster survive. I have this questions:
>>> >
>>> >  1. Since most of my subjects are in the range of 20-35 years and only
>>> >     a few have more than 35 years, it is correct to use the mean?
>>> >     Wouldn’t be better to use the median or some other value?
>>> >  2. This is maybe a silly question, but why should I care about the
>>> >     intercept? I know that demeaning doesn’t change the slope, just
>>> >     the intercept, but I don’t understand why I should care, I read in
>>> >     some publications that demeaning is not always necessary.
>>> >
>>> > Other questions:
>>> >
>>> >  1. Most of my subjects are women (around 80%, N=18), Does it have an
>>> >     advantage to remove the males to get rid of the gender variable?,
>>> >     or should I continue as I’ve been doing, controlling for the
>>> >     effect of gender.
>>> >  2. Since I don’t know if this disease is more aggressive when is
>>> >     developed at some stage of life, I mean, if young patients are
>>> >     less affected than older patients after six years of presenting
>>> >     it, it is correct to control the effect of age? (Entering the
>>> >     variable as a nuisance factor), or should I search for an
>>> >     interaction between age and the condition variable? I only care
>>> >     about differences in cortical thickness.
>>> >
>>> > Any suggestion is very welcome, thanks in advance for your answer.
>>> >
>>> >
>>> > Rodrigo
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> >
>>> > _______________________________________________
>>> > Freesurfer mailing list
>>> > Freesurfer@nmr.mgh.harvard.edu
>>> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
>>>
>>> -- 
>>> Douglas N. Greve, Ph.D.
>>> MGH-NMR Center
>>> gr...@nmr.mgh.harvard.edu
>>> Phone Number: 617-724-2358
>>> Fax: 617-726-7422
>>>
>>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
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>>> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html>
>>> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
>>>
>>> _______________________________________________
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>>>
>>>
>>> The information in this e-mail is intended only for the person to 
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>>> contains patient information, please contact the Partners Compliance 
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>>
>>
>>
>> _______________________________________________
>> Freesurfer mailing list
>> Freesurfer@nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
>
>
>
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> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

-- 
Douglas N. Greve, Ph.D.
MGH-NMR Center
gr...@nmr.mgh.harvard.edu
Phone Number: 617-724-2358
Fax: 617-726-7422

Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2
www.nmr.mgh.harvard.edu/facility/filedrop/index.html
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