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.harvard.
> 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.harvard
> > > > .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/LinearMixedEffect
> > > > > > 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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