On Mo, 2018-04-09 at 19:53 +0200, Kaushal, Mayank wrote: > 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?
Let's consider the model first: I would not recommend to include both 'visit' and 'time_from_baseline' in the same model. This is because these two variables should be highly correlated, and such a redundancy may make the estimation and interpretation of the model difficult. Therefore it is better to include just one of them, but not both. So the basic decision would be whether to treat time 1) as continuous (then use 'time_from_baseline') or 2) as categorical (then use 'visit'): In my eyes, there are advantages and disadvantages to both, see below. Regardless of that, in both cases it will be possible to assess the effects of group, time/visit, and their combination (e.g. different slopes between groups, or presence of group differences only at some visits but not all). Option 1) Using 'time_from_baseline', and hence treating time as continuous, gives the LME model that we have been discussing so far. So there is no need to create a new model, and as far as I can see, your research questions can already be answered with the current model. Although it will not be possible to directly contrast, say, visit 1 and 2 with this approach, you will still be able to make statements like 'Per 1 year (or 2 years, or 1 month, or whatever unit of time, depending on your observation period), we expect changes of magnitude X (or 2*X, or X/12, ...) , according to this model', and this may even be more accurate than expressing change in units of visits. Note, though, that these changes will be modeled as linear changes across time (unless you include additional e.g. quadratic terms in the model). Option 2) Using 'visit', and hence treating time as categorical, would result in a model like a repeated-measures ANOVA. The main advantages, in my eyes, would be that you could explicitly compare between, say, visit 1 and 2, or 1 and 4, etc. So, the categorical model may be a little bit easier to interpret, which is possibly another advantage. A disadvantage of this option is that you'd need to specify a new model. As far as I can see, the LME framework can also cover the categorical scenario, but I'd need to check how. Otherwise, one could use a classical repeated-measures ANOVA (https://surfer.nmr.mgh.harvard .edu/fswiki/RepeatedMeasuresAnova). Another disadvantage is that the categorical approach discards information about the precise timing, and that it makes the assumption that the visits occurred at comparable time intervals across subjects. I thought it would be good to lay out the two main modeling options, as they appear to me now, before proceeding. Best, Kersten > 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.Diers@dz > > ne.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. 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