External Email - Use Caution Hi Martin,
It's been a long time since this discussion but I return on this from now... The problem is that I followed longitudinal images of two groups where I had mainly missing time points at the end. Than you suggested: *If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.* 1) I know the survival analysis toolbox on matlab, but now I would like to know what information will this survival analysis give to me ? 2) Will this analysis tell me if there is a bias ? 3) How to consider early drop-out with this type of analysis based on mass-univariate LME analysis of longitudinal neuroimaging data ? Thanks in advance for helping. Best, Matthieu Le mer. 14 déc. 2016 à 22:14, Martin Reuter <[email protected]> a écrit : > Hi Matthieu, > > 1. yes, LME needs to be done first so that values can be sampled from the > fitted model for the SA. > > 2. yes, I was talking about gradient non-linearities etc that could be in > the image from the acquisition. We currently don’t use non-linear > registration across time points (only rigid). > > Best, Martin > > > On Nov 22, 2016, at 9:31 PM, Matthieu Vanhoutte < > [email protected]> wrote: > > Hi Martin, > > Please see inline below: > > Le 22 nov. 2016 à 17:04, Martin Reuter <[email protected]> a > écrit : > > Hi Matthieu, > (also inline) > > On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte < > [email protected]> wrote: > > Hi Martin, > > Thanks for replying. Please see inline below: > > Le 21 nov. 2016 à 20:26, Martin Reuter <[email protected]> a > écrit : > > Hi Matthieu, > > a few quick answers. Maybe Jorge knows more. > Generally number of subjects / time points etc. cannot be specified > generally. All depends on how noisy your data is and how large the effect > is that you expect to detect. You can do a power analysis in order to > figure out how many subject / time points would be needed. There are some > tools for that in the LME toolbox: > > https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#Poweranalysis > > > 1. see above > 2. yes, also time points can miss from the middle. If you have mainly > missing time points at the end, this will bias your analysis to some > extend, as the remaining ones may be extremely healthy, as probably the > more diseased ones drop out. You may want to do a time-to-event (or > survival-analysis) which considers early drop-out. > > > Is there any way to do with Freesurfer this kind of analysis ? > > > https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis > Yes, there is also a paper where we do this. It is a combination of LME > and Survival Analysis (as for the SA you need to have measurements of all > subjects at all time points, so you estimate that from the LME model). > > > Thank you for the link, I will take a look at. So if understand, this > analysis has to be done after LME statistical analysis ? Thereafter since > SA need all time points, LME model will allow me to estimate missing time > points ? > > > > 3. see above (power analysis) > 4. GIGO means garbage in, garbage out, so the less you QC, the more likely > will your results be junk. The more you QC the less likely will it be junk, > but could still be. The FS wiki has lots of tutorial information on > checking freesurfer recons. For longitudinal, you should additionally check > the surfaces in the base, the brain mask in the base, and the alignment of > the time points (although there is some wiggle space for the alignment, as > most things are allowed to evolve further for each time point). > > > For the alignment of the time points, should I better comparing brainmask > or norm.mgz ? > > > It does not really matter, I would use norm.mgz. I would load images on > top of each other and then use the opacity slider in Freeview to blend > between them (that way the eye can pick up small motions). I would not > worry too much about local deformations which could be caused by > non-linearity (gradient). But if you see global misalignment (rotation, > translation) it is a cause for concern) . > > > Ok thank you. The non-linearity you are talking about are well provoked by > MRI system and not non-linear registration between time points and template > base, aren’t they ? > > Best regards, > Matthieu > > > > In order to avoid bias by adding further time points in the model by the > -add recon all command, is this better for each subject to take into > account all the time points existing for it or only the ones that I will > include in the model (three time points / subject ; if existing 6 time > points for any subject ?) > > > Usually it is recommended to run all time points in the model (so a base > with 6 time points) and not use the - - add flag. Also, Linear Mixed > Effects models deal well with missing time points. It is perfectly OK to > have differently many time points per subject for that. You should still > check if there is a bias (e.g. one group always has 3 time points the other > 6) that would not be good. Maybe also consult with a local biostatistician > if you are not comfortable with the stats. The LME tools are matlab, and so > are the survival-analysis scripts. > > Best, Martin > > > > Best regards, > Matthieu > > > Best, Martin > > On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte < > [email protected]> wrote: > > Dear Freesurfer’s experts, > > I would have some questions regarding the LME model to be used in > longitudinal stream: > > 1) Which are the ratio limits or % of missing timepoints accepted ? > (according time, I have less and less subjects time points) > > 2) Is it possible to include patients that would miss the first timepoint > but got the others ? > > 3) Considering a group in longitudinal study, which is the number of > subjects minimal of this group accepted for LME modeling ? > > 4) Finally, concerning quality control and among a big number of total > time points, which essential controls are necessary ? (Control of norm.mgz > of the base, alignment of longitudinal timepoints on base,… ?) > > Best regards, > Matthieu > > > _______________________________________________ > Freesurfer mailing list > [email protected] > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > _______________________________________________ > Freesurfer mailing list > [email protected] > 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. If you believe this e-mail was sent to you in error and the > e-mail > contains patient information, please contact the Partners Compliance > HelpLine at > http://www.partners.org/complianceline . 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If the e-mail was sent to you in > error > but does not contain patient information, please contact the sender and > properly > dispose of the e-mail. > > > _______________________________________________ > Freesurfer mailing list > [email protected] > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > _______________________________________________ > Freesurfer mailing list > [email protected] > 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. If you believe this e-mail was sent to you in error and the > e-mail > contains patient information, please contact the Partners Compliance > HelpLine at > http://www.partners.org/complianceline . If the e-mail was sent to you in > error > but does not contain patient information, please contact the sender and > properly > dispose of the e-mail. >
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