Hello Martin, Thanks for pointing out about qdec, I missed that part.
We performed this analysis on qdec. Thanks for explaning the advantages of each longitudinal statistic method (http://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalStatistics) ! We are working now with the LME Model. Sincerely, Alex. Le 04/12/2012 7:50 PM, Martin Reuter a écrit : > Hi Alex, > > as far as I know qdec can do what you want (compare e.g. the atrophy > rate across the two groups). > > However, you don't need to use qdec for the second stage, you can run > mri_glmfit directly on the differences (or slopes). Basically you get a > thickness_yearly_change (or pct change) in each base directory and can > then see if that correlates with any covariate or group membership etc. > > Qdec just makes is easier to use. > > Best, Martin > > On Tue, 2012-12-04 at 19:19 -0500, Alex Hanganu wrote: >> Hello Martin, >> >> thanks for the quick reply ! >> >> as I understood, from the "LongitudinalTutorial", after long_mris_slopes >> the results can be seen for each subject, and in order to see the group >> results, all subjects should be analysed in the Qdec, and there I could >> see the results for each group, but couldn't do the inter-group, this is >> why we tried the "RepeatedMeasuresAnova" >> >> Indeed, there is some difference in the time distance, so we will try >> your new idea ! >> >> I know about Jorge's work - Marvellous ! but it seems to need too much >> time to be comprehended and applied. We hoped to be done with these >> results and start already writing the paper :) >> >> Thank you very much for Your help !!! >> >> Sincerely, >> Alex. >> >> >> >> >> Le 04/12/2012 6:44 PM, Martin Reuter a écrit : >>> Hi Alex, >>> >>> I am not familiar with the way Doug describes the repeated measure anova >>> on the wiki. Of course in a longitudinal setting repeated measures are >>> correlated and I am not sure if this is considered in that model there. >>> >>> Since you have only 2 time points, why not simply compare the difference >>> (or weighted by the time distance, if the time points are not the same >>> distance apart)? >>> >>> You would compute (tp2-tp1)/time for each subject and then compare this >>> across groups with a standard glm. >>> Since in the longitudinal stream both thickness maps are registered, you >>> can simply use mris_calc to compute the difference directly. >>> >>> There are also scripts for this (long_mris_slopes, even for more than 2 >>> time points, where we fit a line into each subject). See the >>> http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial >>> This is a simple approach, first reducing the variable of interest >>> (change across time) to a single number per subject and then running a >>> standard test. It should be sufficient for your setting. >>> >>> You can also do more complex modeling using our new linear mixed effects >>> models if you want (see older email from Jorge about that). It considers >>> both temporal and spacial correlation of measures. This model is >>> especially useful if you have differently many time points and time >>> distances per subject. >>> >>> Best, Martin >>> >>> >>> On Tue, 2012-12-04 at 18:26 -0500, Alex Hanganu wrote: >>>> Dear Freesurfer Experts, >>>> >>>> We are analysing longitudinal data - the difference between 2 groups (P >>>> and M) [19 and 17 subjects] with 2 time points for each group (A, B). >>>> >>>> We are using the example of "Repeated Measures Anova" >>>> (http://surfer.nmr.mgh.harvard.edu/fswiki/RepeatedMeasuresAnova) >>>> >>>> For our first approach - we took all the subjects - 36 classes, and >>>> tried to create the contrast for: >>>> >>>> P(B-A) - M(B-A) >>>> 2 within subject factors, and 2 inter-subject >>>> >>>> We considered the recent explanations >>>> (http://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg25459.html), >>>> and, as we understood, our null hypothesis is: >>>> >>>> PB-PA=0 AND MB-MA=0 -> >>>> Combining: PB-PA-MB+MA=0 >>>> >>>> and the matrix seems to be: >>>> 0 0 0 .... (36 zeros) -1 >>>> 0 0 0 .... (36 zeros) 1 >>>> 0 0 0 .... (36 zeros) 1 >>>> 0 0 0 .... (36 zeros) -1 >>>> >>>> but we still get a dimension mismatch between X and C: X has 72, C has 37. >>>> >>>> The fsgd file is like this: >>>> Class subject 1 >>>> . >>>> . >>>> . >>>> Class subject 36 >>>> Variables TP1-vs-TP2 >>>> Input groupPsubj1-A Subject1 -1 >>>> Input groupPsubj1- B Subject1 1 >>>> Input groupPsubj2-A Subject2 -1 >>>> . >>>> . >>>> . >>>> Input groupMsubj35-A Subject35 1 >>>> Input groupMsubj35-B Subject35 -1 >>>> Input groupMsubj36-A Subject36 1 >>>> Input groupMsubj36-B Subject36 -1 >>>> >>>> ============== >>>> >>>> Our second approach - we run mris_glmfit for each group separately and >>>> then we wanted to use mris_calc to compute the difference: >>>> >>>> mris_calc -o avg.mgh groupP/glm-dir/Contrast/sig.mgh add >>>> groupM/glm-dir/Contrast/sig.mgh >>>> >>>> though the sig.mgh for each group shows significant results, the avg.mgh >>>> reveals no effect. >>>> >>>> Can you please help with this analysis ? >>>> >>>> Thanks! >>>> >>>> Sincerely, >>>> Alex. >>>> ____________________ _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. 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