Hi Matthieu,
no, sorry. Maybe you can find a local biostatistician who knows.
A slope estimate with less time points is more uncertain than one with
more time points (obviously). Also a slope estimate with time points on
a line is more certain than one with time points values all over the
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Hi Martin,
I come back on my previous question concerning survival bias in LME.
Do you have an advice ?
Best,
Matthieu
> Le 19 oct. 2018 à 16:30, Matthieu Vanhoutte a
> écrit :
>
> Hi Martin,
>
> Do you mean that high attrition (less time
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Hi Martin,
Do you mean that high attrition (less time points with time) would induce
more variance on the slope estimates but this effect would be compensated
by LME modeling ?
Would there be proportion of early drop outs to respect in order to
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
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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
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
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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
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
Dear FS's experts,
Could you answer me about questions from below inline precedent mail ?
Best regards,
Matthieu
2016-11-22 21:31 GMT+01:00 Matthieu Vanhoutte :
> Hi Martin,
>
> Please see inline below:
>
> Le 22 nov. 2016 à 17:04, Martin Reuter
Hi Martin,
Please see inline below:
> Le 22 nov. 2016 à 17:04, Martin Reuter a écrit :
>
> Hi Matthieu,
> (also inline)
>
>> On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte
>> > wrote:
>>
>> Hi
Hi Matthieu,
(also inline)
> On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte
> wrote:
>
> Hi Martin,
>
> Thanks for replying. Please see inline below:
>
>> Le 21 nov. 2016 à 20:26, Martin Reuter >
Hi Martin,
Thanks for replying. Please see inline below:
> Le 21 nov. 2016 à 20:26, Martin Reuter a écrit :
>
> Hi Matthieu,
>
> a few quick answers. Maybe Jorge knows more.
> Generally number of subjects / time points etc. cannot be specified
> generally. All
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
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
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