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Hi Matthieu,
in LME it is OK if subjects have differently many time points in
general. You need to ask a statistician about your specific setup, but
I think it might be OK (basically less time points, means more variance
on the slope estimates, but that should be considered in LME). But I am
not a statistician.
Best, Martin
On Wed, 2018-10-17 at 18:46 +0200, Matthieu Vanhoutte wrote:
> External Email - Use Caution
> Hi Martin,
>
> Thanks for your answer.
>
> I actually compare neurospychological scores at baseline between
> drop-out subjects and subjects with full time-points. If I ever find
> that drop-out subjects are more severely affected than the subjects
> with full time-points, then there might be a bias in the results of
> my LME study ?
>
> How could I argue that significant patterns found in my LME study
> between both groups are still valid accounting for this bias ? Is LME
> method robust enough for compensating this kind of drop-out ?
>
> Best,
> Matthieu
>
>
> Le mer. 17 oct. 2018 à 18:33, Martin Reuter <mreuter@nmr.mgh.harvard.
> edu> a écrit :
> > Hi Matthieu,
> >
> > 1) survival analysis is typically used if you want to detect if the
> > time to an event is longer in one group vs the other (e.g. one
> > group
> > gets placebo the other drug and we want to know if recurrence is
> > later
> > in the drug group). Not sure this is what you need. The nice thing
> > is,
> > it can deal with drop-outs
> >
> > 2) No, you can directly test that (e.g. do more dieseased drop out
> > than
> > healthy, or are the dropouts on average more advanced (test-scores,
> > hippo-volume etc) than the diseased at baseline... many options.
> > you
> > could also test interactions with age , gender etc. However, not
> > finding an interaction may not mean there is no bias, it is just
> > small
> > enough to go undetected with your data size.
> >
> > 3) Survival analysis is a different analysis than LME.
> >
> > Best, Martin
> >
> > On Tue, 2018-10-16 at 16:15 +0000, Matthieu Vanhoutte wrote:
> > > 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 <mreuter@nmr.mgh.harv
> > ard.
> > > edu> 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
> > <matthieuvanhoutt
> > > > > e@gmail.com> wrote:
> > > > >
> > > > > Hi Martin,
> > > > >
> > > > > Please see inline below:
> > > > >
> > > > > > Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.mgh.har
> > vard
> > > > > > .edu> a écrit :
> > > > > >
> > > > > > Hi Matthieu,
> > > > > > (also inline)
> > > > > >
> > > > > > > On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte
> > <matthieuvan
> > > > > > > houtte@gmail.com> wrote:
> > > > > > >
> > > > > > > Hi Martin,
> > > > > > >
> > > > > > > Thanks for replying. Please see inline below:
> > > > > > >
> > > > > > > > Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh
> > .har
> > > > > > > > vard.edu> 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/LinearMixedEf
> > fect
> > > > > > > > sModels#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
> > <matthieu
> > > > > > > > > vanhoutte@gmail.com> 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
> > > > > > > > >
> > > > > > > > >
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