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Hi Matthieu, > So with the EV1 which is in my case time measure in relation to the > baseline (in days), this EV1 looks like : > > EV1 > Time > 0 > 136 > 0 > 28 > 0 > 427 > 0 > 71 > 0 > 168 > 0 > 399 > 0 > 63 > > With the contrasts [1 0 0 0 ...] and [-1 0 0 0 ...], how could I determine > when Time1 > Time2 or Time2 > Time1 ? > > As coded, if there is positive relationship of time and the FA values (i.e., FA increases over time), the contrast [1 0 0 0 ...] will be significant. For decreases in FA over time, [-1 0 0 0 ...] will be significant. All the best, Anderson > >> >> >> >>> >>>> Another possibility is to consider the sandwich estimator, available in >>>> an SPM toolbox >>>> <http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/software/swe> >>>> by Bryan Guillaume and Tom Nichols, which bypasses some issues related to >>>> compound symmetry and missing data. You may want to read the documentation >>>> and see if it applies to your case. >>>> >>>> >>> Ok, thanks I will look at this sandwich estimator. >>> >>> >>>> All the best, >>>> >>>> Anderson >>>> >>> >>> Best regards, >>> >>> Matthieu >>> >>> >>>> >>>> >>>> On 30 September 2015 at 08:47, Matthieu Vanhoutte < >>>> [log in to unmask]> wrote: >>>> >>>>> Hi Anderson, >>>>> >>>>> Indeed I reduced my patients list because of quality of input images, >>>>> and now I have 7 patients in one group with 2 up to 4 timepoints non >>>>> regularly spaced in time : >>>>> >>>>> - patient1: 2 timepoints >>>>> - patient2: 3 timepoints >>>>> - patient3: 2 timepoints >>>>> - patient4: 4 timepoints >>>>> - patient5:4 timepoints >>>>> - patient6: 3 timepoint >>>>> - patient7: 3 timepoints >>>>> >>>>> Could you explain me how to use longitudinal TBSS with these data >>>>> (define inputs, design matrix and contrasts) ? >>>>> >>>>> Best regards, >>>>> >>>>> Matthieu >>>>> >>>>> >>>>> 2015-09-29 11:41 GMT+02:00 Matthieu Vanhoutte < >>>>> [log in to unmask]>: >>>>> >>>>>> Hi Anderson, >>>>>> >>>>>> I would like to investigate for example FA reduction with time. The >>>>>> number of timepoints is varied because of the clinical nature of the study: >>>>>> some patients came one time whereas others came up to seven times. >>>>>> Moreover, time between two timepoints aren't regular between >>>>>> patients. I have 11 patients with the following timepoints: >>>>>> >>>>>> - patient1: 3 timepoints >>>>>> - patient2: 3 timepoints >>>>>> - patient3: 2 timepoints >>>>>> - patient4: 3 timepoints >>>>>> - patient5: 7 timepoints >>>>>> - patient6: 1 timepoint >>>>>> - patient7: 3 timepoints >>>>>> - patient8: 2 timepoints >>>>>> - patient9: 7 timepoints >>>>>> - patient10: 3 timepoints >>>>>> - patient11: 3 timepoints >>>>>> >>>>>> Do you think it is possible to make longitudinal TBSS on these >>>>>> irregular timepoints subjects and in this case how define correctly input >>>>>> and model for statistical analysis ? >>>>>> >>>>>> Thanks in advance for helping ! >>>>>> >>>>>> Best regards, >>>>>> >>>>>> Matthieu >>>>>> >>>>>> >>>>>> 2015-09-29 11:02 GMT+02:00 Anderson M. Winkler < >>>>>> [log in to unmask]>: >>>>>> >>>>>>> Hi Matthieu, >>>>>>> >>>>>>> The "evolution" is too much a broad term. What exactly do you want >>>>>>> to investigate? Could you explain the why the number of timepoints is >>>>>>> varied? Is the missingness only towards the end, or are there gaps in the >>>>>>> middle? Please, give as much detail as possible. >>>>>>> >>>>>>> All the best, >>>>>>> >>>>>>> Anderson >>>>>>> >>>>>>> >>>>>>> On 29 September 2015 at 09:49, Matthieu Vanhoutte < >>>>>>> [log in to unmask]> wrote: >>>>>>> >>>>>>>> Dear FSL's experts, >>>>>>>> >>>>>>>> I have one group and variable number of timepoints per subject >>>>>>>> (from 1 up to 7) and would like to assess the evolution according >>>>>>>> time-points of DTI parameters (FA, MD, ...). >>>>>>>> >>>>>>>> Concerning the statistical analysis, how should I proceed in terms >>>>>>>> of inputs and models of the longitudinal variable number of timepoints ? >>>>>>>> >>>>>>>> Many thanks in advance for helping !! >>>>>>>> >>>>>>>> Best regards, >>>>>>>> Matthieu >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >