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,
- 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
- 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.
- 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.
- 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,
- 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%C2%A0 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 > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer@nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/fre
esur
> > > fer > > > > > > > > > > _______________________________________________ > > Freesurfer mailing list > > Freesurfer@nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/frees
urfe
> > r > > > > > > 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%C2%A0. If the e-mail
was
> > sent to you in error > > but does not contain patient information, please
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> > the sender and properly > > dispose of the e-mail. > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesur
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