I am trying to determine the optimum FLAME design for analyzing a
multi-subject, multi-session (e.g. pre & post treatment) multi-group (e.g.
treatment 1, treatment 2, control) & multivariate (difference between
treatment 1, 2 & control; and difference between treatment success) data
set. For example, all subjects were scanned under identical conditions
prior to and following treatment. A small portion of each group was
responsive to treatment while the rest were not. I have attached a text
file containing a condensed example of the current design I am using for
your reference.
There are only a limited number of successful responders in each group (e.g.
Cont only has 1 successful responder). It appears from the results that
each group is being weighted equally, which is causing the smaller groups
(e.g. N=1 for Cont-success) to over influence the results.
Is there a different design that will account for this error? Or, is there a
way to assess each variate independently (i.e. effect of treatment type;
effect of success) while controlling for the effects of the alternate variate?
Thanks for the help
Chris
