Dear all,
I'm attempting to correlate functional effects of medication treatment to
behavioral data. The study involves 18 patients, 3 sessions for each patient
(corresponding to 3 medication regimes (A, B, C); so 3 measures for each
patient).
For this, I specified a higher level design involving 54 inputs (18 x 3), 1
group, and 24 EVs. The first two EVs code for (randomized) medication
regimes (B > A and C > A respectively; contrasts between these EVs yields C
<> B). The second two EVs code for scanorder (session 2 > 1; session 3 > 1
respectively). Next two EVs code for testversion (2 > 1 and 3 > 1
respectively; randomized); The remaining 18 EVs put weights to data derived
from the same subject (to account for pairedness of the data), i.e.:
Inputs: Group EV1 EV2 EV3 EV4 EV5 EV6 EV7 EV8
EV9....
1 1 -1 -1 -1 -1 0 1 1 0
0
2 1 0 1 1 0 -1 -1 1 0
0
3 1 1 0 0 1 1 0 1 0
0
4 1 0 1 -1 -1 1 0 0 1
0
5 1 -1 -1 1 0 0 1 0 1
0
6 1 1 0 0 1 -1 -1 0 1
0
7 ... 54: Etc.
Now, I want to add an additional EV coding for (demeaned) MMSE scores (a
neuropsychological scale), e.g.:
EV 55:
3.5
3.5
3.5
-2.5
-2.5
-2.5
...etc
The design however becomes rank-deficient (something to the power of -17)
as soon as I add this EV.
I tried various approaches, the first one being to collapse my data by
calculating differential images of regime-types (i.e. B > A and C > A) in a
second level analysis, and entering these images as inputs in a third-level
analysis for correlation with behavioral data. However, as soon as I try to
regress out effects of either scanorder or testversion at second level (at
which level the differential images are calculated), the design becomes rank
deficient (again something ^ -17). I therefore stuck to calculating
differential images at 2nd level and then tried to regress out effects of
scanorder and testversion at 3rd level, while at the same time correlating
data with behavioral scores.
For this, I separated differential inputs (B > A) from (C > A) inputs and
ran two separate third level analyses on these separate datasets. I thought
this to be necessary, since putting these inputs together in one design
would require additional EVs coding for pairedness of inputs derived from
the same subject. This would bring the total number of EVs to 27, with
number of inputs = 36, which I thought would not work well (please correct
me if I'm wrong).
Both separate 3rd level analyses worked well, however by now, collapsing of
data had reduced the total number of inputs for each design to 18, while the
number of EVs was 8. By now, nothing of my original effects of treatment
survived (effects are rather small). Might this be due to data-collapsing
and loss of DOF? Do I just demand too much of my data, or would there
perhaps be an alternative solution to this problem? Any help would be
greatly appreciated,
Thanks,
Rutger.
Drs. R. Goekoop, MD.
Department of Neurology
Vrije Universiteit Medical Centre
De Boelelaan 1117, P.O. Box 7057
1007 MB Amsterdam, the Netherlands
Phone: 0031-20-4440316
E-mail: <mailto:[log in to unmask]>
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