External Email - Use Caution 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 <matthieuvanhou...@gmail.com> a > écrit : > > 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 > compensate bias with LME ? > > Thanks, > Matthieu > > > Le ven. 19 oct. 2018 à 16:19, Martin Reuter <mreu...@nmr.mgh.harvard.edu > <mailto:mreu...@nmr.mgh.harvard.edu>> a écrit : > 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 <mreu...@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 <mreu...@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 <mailto:e...@gmail.com>> wrote: > > > > > > > > > > > > Hi Martin, > > > > > > > > > > > > Please see inline below: > > > > > > > > > > > > > Le 22 nov. 2016 à 17:04, Martin Reuter <mreu...@nmr.mgh.har > > > vard > > > > > > > .edu> a écrit : > > > > > > > > > > > > > > Hi Matthieu, > > > > > > > (also inline) > > > > > > > > > > > > > > > On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte > > > <matthieuvan > > > > > > > > hou...@gmail.com <mailto:hou...@gmail.com>> wrote: > > > > > > > > > > > > > > > > Hi Martin, > > > > > > > > > > > > > > > > Thanks for replying. Please see inline below: > > > > > > > > > > > > > > > > > Le 21 nov. 2016 à 20:26, Martin Reuter <mreu...@nmr.mgh > > > .har > > > > > > > > > vard.edu <http://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 > > > > > > > > > <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 > > > > > > > <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 > > > > > > > > > > vanhou...@gmail.com <mailto:vanhou...@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 > > > > > > > > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > > > > > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/fre > > > > > > > > > > <https://mail.nmr.mgh.harvard.edu/mailman/listinfo/fre> > > > esur > > > > > > > > > > fer > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > > > > Freesurfer mailing list > > > > > > > > > Freesurfer@nmr.mgh.harvard.edu > > > > > > > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > > > > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/frees > > > > > > > > > <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. 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