Hi Katarina,
different time spacing is OK (not optimal, but OK). This 2-stage
procedure first fits a line in each subject, independent on how many
time points the subject has. Of course a linear fit from 4 time points
will be more reliable than one from just 2 time points. This is not
taken into consideration and therefore we usually recommend the Linear
Mixed Effects modeling (where it is considered in the model). If most of
your subjects have 4 time points and the spacing is similar (across
subjects) you should be fine.
Other things I noticed:
- you should drop the --generic-time flag. I probably should change the
help text to be more specific, but what it does it assigns a time of 1 2
3 4 to the time points (this is if you do repeated measures and there is
no real time). So don't pass it.
- you can drop some of the --do... flags. Probably you are interested in
the rate and one of the pct change flags. The average is just the
average thickness (across time) for each subject. Not sure you want to
analyze that. The --do-stack is a subject-specific stack of the time
points, usually only used for debugging.
- the -stack-avg is not needed unless you want to analyze average
thickness for each subject, rather you may want to create a stack for
the rate or the pc1 or whatever, to pass it into the stats tool (like
mri_glmfit, or R, SPSS or whatever you use).
Best, Martin
On 09/12/2016 12:40 PM, Katarina Trojacanec wrote:
Hi,
I have a question about the analysis of rates or percent changes.
I use data with available scans at baseline (TP1) and the 6-month
(TP2), 12-month (TP3) and 24-month (TP4) follow-ups from ADNI dataset.
An example of some of the data in the appropriate qdec table is given
below:
fsid fsid-base years age weight diagnosis gender Glob_CDR NPI-Q_TotScr
MMSE_TotScr FAQ_TotScr visit1_2_3_4
ADNI_sub1_sc ADNI_base1 0 81.3 y AD M x x x x sc
ADNI_sub1_m06 ADNI_base1 0.528767123 81.9 y AD M x x x x m06
ADNI_sub1_m12 ADNI_base1 1.030136986 82.4 y AD M x x x x m12
ADNI_sub1_m24 ADNI_base1 2.030136986 83.4 y AD M x x x x m24
ADNI_sub2_sc ADNI_base2 0 74 y AD F x x x x sc
ADNI_sub2_m06 ADNI_base2 0.501369863 74.5 y AD F x x x x m06
ADNI_sub2_m12 ADNI_base2 1.005479452 75 y AD F xx xx m12
ADNI_sub2_m24 ADNI_base2 2 76 y AD F xx xx m24
The base is constructed using all four time points. The time variable
is given in years. I am using long_stats_slopes for aseg.stats as follows:
long_stats_slopes --qdec ./qdec/long_AD_NL_TP1_2_3_4.qdec.table.dat
--stats aseg.stats --meas volume --sd $SUBJECTS_DIR --do-avg --do-rate
--do-pc1fit --do-pc1 --do-spc --do-stack --generic-time --time years
--stack-avg ./qdec/long_AD_NL_TP1_2_3_4
(similarly for ?h.aparc.stats)
Having in mind that TP1 and TP2, as well as TP2 and TP3 are separated
approximately 6 months and TP3 and TP4 are separated approximately 12
months, are the annualized percent change or atrophy rates using this
scenario reasonable (statistically?). Is it maybe more reasonable to
apply the same scenario using three time points (for example TP1, TP3
and TP4 from the previous example and the template based only on these
time points with the same approximate difference between all of them
of 12 months, or TP1, TP2 and TP3 from the previous example and the
template based only on these time points with the same approximate
difference between all of them of 6 months)?
Best Regards,
*Katarina Trojacanec, M.Sc.*
Teaching and research assistant
Faculty of Computer Science and Engineering
Ss. Cyril and Methodius University - Skopje, Republic of Macedonia
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--
Martin Reuter, PhD
Assistant Professor of Radiology, Harvard Medical School
Assistant Professor of Neurology, Harvard Medical School
A.A.Martinos Center for Biomedical Imaging
Massachusetts General Hospital
Research Affiliate, CSAIL, MIT
Phone: +1-617-724-5652
Web : http://reuter.mit.edu
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