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Re: covariates of no interest at higher level

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Mon, 26 Jan 2004 09:02:50 +0100

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 ```Hi Steve, Thanks for the reply. This is what I did first (without age as covariate): Group EV1 EV2 EV3 1 1 0 0 1 1 0 0 2 0 1 0 2 0 1 0 3 0 0 1 3 0 0 1 Contrasts: A-B = EV1-EV2 = 1 -1 0 A-C = EV1-EV3 = 1 -1 0 B-C = EV2-EV3 = 0 1 -1 Unfortunately, this model does not allow me to include one extra covariate for age, since I have 3 groups, and each EV should contain non-zero values for one group only, the other values must be zero.... So the best model with age as covariate I could think of, was to pretend as if all subjects belong to 1 group: Group EV1 EV2 EV3 (age demeaned) 1 1 1 0.25 1 1 1 0.25 1 -1 0 -0.75 1 -1 0 0.5 1 0 -1 -0.5 1 0 -1 0.25 Contrasts: A-B = EV1 = 1 0 0 A-C = EV2 = 0 1 0 B-C = EV2-EV1 = -1 1 0 Is this how I should do it, or is there a better way to include age as covariate in one model? I would prefer modelling three different groups instead of just one. Thanks again, Serge. -----Original Message----- From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On Behalf Of Stephen Smith Sent: Monday, January 26, 2004 6:24 AM To: [log in to unmask] Subject: Re: [FSL] covariates of no interest at higher level Hi Serge. Correlating with age in a separate analysis isn't too useful - doesn't really tell you anything about the main analysis. Just because the effect is under threshold doesn't mean there isn't some correlation with age. So I would do the analysis all in one model. I'm not quite sure how you modelled age in the all-in-one analysis. I am assuming that the main part of the model has one EV each for A, B and C, with unpaired t-tests carried out with contrasts between these EVs. I would have thought the first thing to try with the age confound is to just add one extra EV with all the ages in it (but it must then be demeaned before putting into the model). Is this what you tried? Cheers, Steve. On Fri, 23 Jan 2004, Rombouts, S.A.R.B. wrote: > Hi, > > > > In a higher level FEAT analysis, I have 3 groups (A, B, C) and used a > 3-sample t-test to test for differences between groups (A-B, A-C, B-C). > > Since age may be a confounding factor in each comparison, I additionally > correlated data across groups with age (that is A + B (one group) with age > as EV, A+C with age, and B+C with age). This appeared not to be the case. > > > > Alternatively, when I analyse the data in a different way, with all EVs in > one analysis (that is, EVs testing for group differences, and one EV > representing all ages), many group differences I saw in the first analysis, > disappear. > > > > I am confused about the proper way to analyse my data: which of the two > results is correct? Is it enough to check whether a covariate significantly > explains group differences in a separate analysis, or must I include all EVs > (both those of interest and those of no interest) in one big analysis? > > > > Thanks, > > Serge. > > > >  Stephen M. Smith DPhil  Associate Director, FMRIB and Analysis Research Coordinator  Oxford University Centre for Functional MRI of the Brain  John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK  +44 (0) 1865 222726 (fax 222717)  [log in to unmask] http://www.fmrib.ox.ac.uk/~steve ```

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