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:

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.

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).

On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte <matthieuvanhoutte@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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