Hi Mailing List, I am fitting an LME model with random effects for B0 and B2, so I am using the following to fit a spatiotemporal model:
lhstats = lme_mass_fit_Rgw(X,[1 3],Y,ni,lhTh0,lhRgs,lhsphere); However, prior to this when i am computing the initial temporal covariance estimates, do the square bracketed numbers refer to the random effects as well? So would this be run for random effects at B0 and B2: [lhTh0,lhRe] = lme_mass_fit_EMinit(X,[1 3],Y,ni,lhcortex,3); Kind regards, Bronwyn Overs Research Assistant Neuroscience Research Australia Margarete Ainsworth Building Barker Street Randwick Sydney NSW 2031 Australia M 0411 308 769 T +61 2 9399 1883 F +61 2 9399 1265 neura.edu.au <http://neura.edu.au/> <https://twitter.com/neuraustralia> <https://www.facebook.com/NeuroscienceResearchAustralia> <http://www.neura.edu.au/help-research/subscribe> > On 7 Mar 2017, at 11:57 pm, Martin Reuter <mreu...@nmr.mgh.harvard.edu> wrote: > > Hi Bronwyn, > > to shorten equations, lets set t = years_form_baseline > a = age > g = group > s = sex > > so your model is > Y_ij = b0 + b1 t_ij + b2 a_i + b3 g_i + b4 s_i + b5 t_ij a_i + b6 t_ij g_i + > b7 a_i g_i + b8 t_ij a_i g_i > (as a fist step, I would consider simplifying it, by dropping the age > interactions). > > Anyway > > for male (s_i =0) and controls (g_i = 0) this reduces to > Y_ij = b0 + b1 t_ij + b2 a_i + b5 t_ij a_i > so b1 is the slope for male controls, controlling for age and the slope age > interaction. (0 1 0….) > > Now for female (s=1) patients (g=1) we get this: > > Y_ij = b0 + b1 t_ij + b2 a_i + b3 + b4 + b5 t_ij a_i + b6 t_ij + b7 a_i + b8 > t_ij > = (b0+b3+b4) + (b1 + b6 + b8 ) t_ij + (b2+b7) a_i + b5 t_ij a_i > So the slope for female patients (controlling for age and age time > interaction) would be > (b1 + b6 + b8) > 0 1 0 0 0 0 1 0 1 > > The difference in slope between female patients and male controls would be > 0 0 0 0 0 0 1 0 1 (or the negative of that depending which way you subtract). > Similarly you can look at group differences (controlling for age gender and > interactions). > > Always write out the full model to make sure you understand what you are > doing. > > To complete the picture, here is the contrast for the slope of male patients > 0 1 0 0 0 0 1 0 1 (it is the same as for female patients, because you don’t > have a timeXgender interaction. So that is your patient slope ) > Therefore the > 0 0 0 0 0 0 1 0 1 is the slope difference between the groups. > > I would recommend you talk to a local biostatistician, to make sure you are > actually modelling what you want to model. And that you are interpreting the > results correctly. > > Grüße, Martin > >> On 06 Mar 2017, at 18:56, Bronwyn Overs <b.ov...@neura.edu.au >> <mailto:b.ov...@neura.edu.au>> wrote: >> >> Hi Martin, >> >> Thank you for your response, that is much clearer. >> >> I am also a little confused about how to specify the exact contrasts we wish >> to test and was hoping to get some advice. My design matrix X includes the >> following columns: >> 1. Intercept >> 2. Years from baseline >> 3. Age at baseline >> 4. Group (patients labelled 1, controls 0) >> 5. Gender (females labelled 1, males 0) >> 6. Col 2 (years) * Col 3 (age) >> 7. Col 2 (years) * Col 4 (group) >> 8. Col 3 (age) * Col 4 (group) >> 9. Col 2 (years) * Col 3 (age) * Col 4 (group) >> >> If I test the following contrast, is it giving me the effect of years across >> all groups and genders, or just years for male controls: >> CM.C = [0 1 0 0 0 0 0 0 0] >> >> Also, what contrast should I use to examine the effect of years in my >> patient group irrespective of gender? >> >> Kind regards, >> Bronwyn Overs >> Research Assistant >> >> Neuroscience Research Australia >> Margarete Ainsworth Building >> Barker Street Randwick Sydney NSW 2031 Australia >> M 0411 308 769 T +61 2 9399 1883 F +61 2 9399 1265 >> >> neura.edu.au <http://neura.edu.au/> >> <https://twitter.com/neuraustralia> >> <https://www.facebook.com/NeuroscienceResearchAustralia> >> <http://www.neura.edu.au/help-research/subscribe> >>> On 4 Mar 2017, at 12:43 am, Martin Reuter <mreu...@nmr.mgh.harvard.edu >>> <mailto:mreu...@nmr.mgh.harvard.edu>> wrote: >>> >>> Hi Bronwyn, >>> >>> I think years-between-scans should be years-from-baseline-scans . You may >>> need to compute that if what you have is really years between neighbouring >>> scans. >>> >>> 1. Usually people use intercept and maybe years-from-baseline as random >>> effects. I would not include too many random effects, as it each adds a lot >>> of free parameters and you need a lot of data to fit all that in a >>> meaningful way. Which of your columns are random effects can be passed >>> lme_fit_FS(X,[1 2],Y(:,1)+Y(:,2),ni); >>> for example has column 1 and 2 as random effects. >>> >>> 2. You can do a model comparison as described on our wiki >>> https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels >>> <https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels> >>> >>> You run the more complex model first (do the EM init and maybe RgGrow and >>> RgW fit) and then the simple one (only the EMinit and RgW fit) and do a >>> likelihodd ratio test. An example is on the above wiki. >>> >>> Best ,Martin >>> >>> >>> >>> >>> >>>> On 27 Feb 2017, at 04:16, Bronwyn Overs <b.ov...@neura.edu.au >>>> <mailto:b.ov...@neura.edu.au>> wrote: >>>> >>>> Dear mailing list, >>>> >>>> I am trying to run a LME model using the matlab tools, but I’m unsure how >>>> to specify the model we wish to run. We have a qdec file that contains the >>>> following columns: >>>> fsid, fsid-abse, years between scans, age at baseline, gender, group >>>> >>>> We want to specify a model where we can examine four interaction terms >>>> (years*age, years*group, age*group, years*age*group), as well as random >>>> effects for the intercept, years and age. My questions are: >>>> 1. How do we specify a model that will include the random effects we want? >>>> 2. How do we compare our full model (3 random effects) with a model >>>> excluding the random effect for age? >>>> >>>> Kind regards, >>>> Bronwyn Overs >>>> Research Assistant >>>> >>>> Neuroscience Research Australia >>>> Margarete Ainsworth Building >>>> Barker Street Randwick Sydney NSW 2031 Australia >>>> M 0411 308 769 T +61 2 9399 1883 F +61 2 9399 1265 >>>> >>>> neura.edu.au <http://neura.edu.au/> >>>> <https://twitter.com/neuraustralia> >>>> <https://www.facebook.com/NeuroscienceResearchAustralia> >>>> <http://www.neura.edu.au/help-research/subscribe> >>>> _______________________________________________ >>>> Freesurfer mailing list >>>> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> >>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>> <https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer> >>> _______________________________________________ >>> Freesurfer mailing list >>> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>> <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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