I am indeed considering LME model since I have multiple visits (4) for each subject and multiple groups (3).
Based on your response, I am inclined to include the following. Intercept - random variable Slope (time_base_scan) - random variable As I am including time_base_scan as the random variable, this should take care of the timing information. Am I correct in making this assumption? Moving on to the categorical variables - group and visit. I want to evaluate the following effects 1. group 2. visit 3. group and visit My concern here is how do I design the contrast matrix for this? I am aware that I have asked more than my fair share of questions and very grateful to you for being exceedingly patient in answering them. Mayank > On Apr 9, 2018, at 12:15 PM, Diers, Kersten /DZNE <kersten.di...@dzne.de> > wrote: > > ATTENTION: This email originated from a sender outside of MCW. Use caution > when clicking on links or opening attachments. > ________________________________ > > Hello, > > this is somewhat difficult to answer, so the following is a personal > opinion. > > I think the first question is whether your would like to go for a) a > cross-sectional design or b) a longitudinal design. > > If a), you'd have a single scan per subject (e.g., baseline) and could > discard the temporal information. So if you do not have a longitudinal > research question and your goal is to model group differences at a > single time-point, it is not necessary (and overly complicated, > actually) to use a LME model. > > If b), you'd have multiple scans per subject, and would therefore use > the LME model. I would not recommend to exclude the timing information > from the model / design matrix in this case, no matter how close the > temporal spacing may be. It is still possible to test for effects other > than time, e.g. for group differences at baseline. > > Best regards, > > Kersten > > On Mi, 2018-04-04 at 23:37 +0200, Kaushal, Mayank wrote: >> The continuous variable in my analysis is time_base_scan with the >> time points evaluated by me being of acute and subacute nature. >> However, due to the small differences in time elapsed between >> successive visits (duration between visits is a couple of weeks), is >> it possible to simply model LME analysis around categorical variables >> without including continuous variables? >> >> More specifically, I want to see the effect of group. Further, I >> don’t intend to include time_base_scan as a continuous variable. >> What would the contrasts used by me to evaluate a categorial >> variable? >> >> Further, my analysis includes three groups. So, my understanding is >> that evaluating group as a categorical variable would’ve no bearing >> on the column composition of X and they would remain the same as >> mentioned in my previous mail. >> >> The columns in X are as follows: >> Column 1: intercept >> Column 2: time_base_scan (This signifies the time elapsed from the >> base scan in days. Each subject had upto 4 visits with scans >> undertaken on each visit. For the scan taken on the >> first visit 0 was entered in the qdec.dat.table file) >> Column 3: age1stscan (This is the age of the subject at first scan) >> Column 4: group 1 (Value of 1 entered in this column if the subject >> belonged to group 1 and value of 0 entered in column 5 that signifies >> group 2) >> Column 5: group 2 (Value of 1 entered in this column if the subject >> belonged to group 2 and value of 0 entered in column 4 that >> signifies group 1) >> >> Mayank >> On Mar 30, 2018, at 6:30 AM, Diers, Kersten /DZNE <Kersten.Diers@dzne >> .de<mailto:kersten.di...@dzne.de>> wrote: >> >> ATTENTION: This email originated from a sender outside of MCW. Use >> caution when clicking on links or opening attachments. >> ________________________________ >> Hello, >> >> please find my responses below. >> >> Best regards, >> >> Kersten >> >> On Do, 2018-03-29 at 00:11 +0200, Kaushal, Mayank wrote: >> >> Hi Kersten, >> >> Apologies for the delay. I am still in the process of trying to >> figure out the gaps in my understanding and would appreciate your >> inputs. >> >> I created the design X from M using: X = [ones(length(M),1) M >> M(:,1).*M(:,3) M(:,1).*M(:,4)]; >> >> The columns in X are as follows: >> Column 1: intercept >> Column 2: time_base_scan (This signifies the time elapsed from the >> base scan in days. Each subject had upto 4 visits with scans >> undertaken on each visit. For the scan taken on the >> first visit 0 was entered in the qdec.dat.table file) >> Column 3: age1stscan (This is the age of the subject at first scan) >> Column 4: group 1 (Value of 1 entered in this column if the subject >> belonged to group 1 and value of 0 entered in column 5 that signifies >> group 2) >> Column 5: group 2 (Value of 1 entered in this column if the subject >> belonged to group 2 and value of 0 entered in column 4 that >> signifies group 1) >> Column 6: Column 2 (time_base_scan) x Column 4 (group 1) >> Column 7: Column 2 (time_base_scan) x Column 4 (group 2) >> >> So my understanding is that subjects belonging to group 3 have 0 in >> both columns 4 and 5 and consequently, columns 6 and 7 would also be >> 0 for them. This would be translated by matlab lme toolbox while >> doing spatiotemporal analysis. Is my understanding correct? >> >> >> Yes, this is correct, apart probably from a little typo: >> >> Column 7 should read: " Column 2 (time_base_scan) x Column 5 (group >> 2)" >> (group2 is column 5, not 4, in X; please adapt your design matrix if >> necessary) >> >> Group 3 will be your reference group then, and will implicitly >> modeled by this design matrix. I speculate this is what you mean by >> 'translated'. >> >> >> I have attached X as well as M as separate excel files for your >> consideration. >> In addition, I have attached qdec.dat.table file as well a word doc >> labeled “workflow” that detail the order of matlab functions I have >> used to perform the analysis. >> >> >> >> My objective is to highlight clusters that are significantly >> different between the groups over time using spatiotemporal analysis. >> >> The contrasts used by me for the analysis: CM.C = [0 0 0 0 0 1 0; 0 0 >> 0 0 0 -1 1] >> >> Kindly comment on my choice of contrasts. Is this the correct choice >> for contrasts if I want to find significant clusters based on >> cortical thickness between all three groups over time? >> >> >> >> Yes, the contrast is also correct for your purpose. It will identify >> regions in which cortical thickness changes across time differ >> between groups. >> >> >> Mayank >> >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> >> https://urldefense.proofpoint.com/v2/url?u=https-3A__mail.nmr.mgh.har >> vard.edu_mailman_listinfo_freesurfer&d=DwIBAg&c=aFamLAsxMIDYjNglYHTMV >> 0iqFn3z4pVFYPQkjgspw4Y&r=RqvEwdmcEg_aWsE7PBL80w&m=jz6TYzDc4SmT6ar1_JE >> w5rF4JCOlAxM5KmH1GK_SDNc&s=9INo1YCHATevCPV_UsV08YkkiXAOEcUSw7nvJLvyBa >> g&e= >> >> >> The information in this e-mail is intended only for the person to >> whom it is >> addressed. 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