Hi VM, They can stay all in the same .csv file. The "fwep" files will have p-values corrected across all columns of this input.
All the best, Anderson On 2 February 2017 at 12:09, neuroimage analyst < neuroimage.anal...@gmail.com> wrote: > Thanks, Anderson. If there are several other measures such as volume, > thickness, FA, can I then put them in a single csv file and run the > command or I have to create 3 csv files, one each for volume, thickness,and > FA and also invoke -corrmod? > > Thanks > > Regards > VM > > On Feb 2, 2017 1:01 AM, "Anderson M. Winkler" <wink...@fmrib.ox.ac.uk> > wrote: > > Hi VM, > > Please see below: > > > On 1 February 2017 at 02:38, neuroimage analyst < > neuroimage.anal...@gmail.com> wrote: > >> Hi, >> >> We extracted volume measures from 7 ROIs in 2 groups and compared >> a) Mean of each ROI volume between group, and compared independently such >> as >> i) whether mean of ROI 1 in group 1 is equal to mean of ROI1 in group 2 >> ii)whether mean of ROI 2 in group 1 is equal to mean of ROI2 in group 2 >> and so on >> > > This means 7 comparisons, or 14 if you test both directions (tails) > separately. > > >> >> b) Plotted the mean ROI volume for 2 independent measures and compared >> whether the slope between and within group is significantly different. such >> as >> i) Slope of ROI1 is positively associated with MoCA , age (grp1) ? >> ii) Slope of ROI1 is negatively associated with MoCA, age (grp2) ? >> iii) Slope of ROI1 vs MoCA (grp1) is significantly different >> than slope of ROI1 vs MoCA (grp2) ? >> iv) Slope of ROI1 vs MoCA (grp2) is significantly different than slope of >> ROI2 vs MoCA (grp2) ? >> > > These sound tests for main effects and interaction, with various > combinations possible. > > >> >> Q1) Do we have to perform multiple comparisons for (a) and (b)? >> > > Yes. > > > >> Q2) If yes, do we correct across 7 measurements for (a) and 2 >> measurements for (b) or 7 measurements for both (a) and (b)? >> > > You could put the values for the 7 ROIs in a table in .csv format, with > one column per ROI and one row per subject. This would be the input data. > Create a design matrix as: > > EV1: 0 or 1 coding for group 1 > EV2: 0 or 1 coding for group 2 > EV3: MoCA group 1 > EV4: MoCA group 2 > EV5: Age group 1 > EV6: Age group 2 > etc > > Then define a set of contrasts such as: > > C1: [1 -1 0 0 0 0 ...] - This tests if mean for group 1 > mean for group 2 > C2: [-1 1 0 0 0 0 ...] - This tests if mean for group 1 < mean for group 2 > C3: [0 0 1 -1 0 0 ...] - This tests if the slope for MoCA for group 1 > > slope for MoCA for group 2 > C4: [0 0 -1 1 0 0 ...] - This tests if the slope for MoCA for group 1 < > slope for MoCA for group 2 > C5: [0 0 1 0 0 0 ...] - This tests if slope for MoCA for group 1 > 0 > C6: [0 0 -1 0 0 0 ...] - This tests if slope for MoCA for group 1 < 0 > etc > > You'd run this analysis in PALM with something as: > > *palm -i input.csv -d design.mat -t design.con -corrcon -o myresults -logp* > > > Hope this helps! > > All the best, > > Anderson > > > >> There is evidence to both performing and nonperforming multiple >> comparisons In the literature. >> >> Any response (hopefully pointing to a literature) will be greatly >> appreciated. >> >> Thanks >> >> Regards >> >> --VM >> >> _______________________________________________ >> 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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