Dear FS list, (dear Martin!)
I have a longitudinal design, 3 groups, 2 timepoints, and I created three different QDEC tables (two groups at a time) in order to be able to compare each pair of groups in terms of percentage change from pre to post (what's referred to as "pc1" in the first stage of the two-stage-model). I look at thickness, area and lGI as dependent variables. Group is a categorical factor of interest, gender as the other categorical factor (not interested in its effect), and age as (continuous) nuisance. I'm interested in the contrast "Does the average ..., accounting for gender, differ between G1 and G2?"
Say that, for some analysis (e.g. group 1 vs group 2, thickness, LH) I get a certain cluster that survives the MonteCarlo Null-Z correction. If the cluster is redish, I take it to mean that group 1 has a higher signed value of pc1 than group 2, and vice-versa if the cluster is blueish. However, because pc1 is (I think) a signed statistic, i.e. is negative for thickness decrease and positive for thickness increase, this makes me unsure how to interpret the stage2 (cross-sectional) result. As I understand it, having a significant red cluster in the group comparison can mean either of these 3 scenarios:
1. Both groups have pc1>0 2. G1 has pc1>0 and G2 has pc1<0 3. Both groups have pc1<0
..but that the pc1 of G1 is always greater, as a signed number, than that of G2, since that is what defines the cluster.
The problem is that, when I run the same analysis but keeping only subjects of one group in at one time, i.e. for each group separately and looking at the contrast "Does the average ... differ from zero?", in most cases I don't get the same cluster that appeared in the group comparison.
My question is therefore: can a cluster appearing in the between-groups analysis be trusted if the same (or similar) cluster does *not *appear in each of the two within-groups (one-sample test) analyses? And if the latter analysis produces null results but the former does have sig clusters, does it still make sense to look at the sign of each group's mean pc1 in order to find the effect of the longitudinal treatment (atrophy or increase), even if that group in fact did not have a pc1 that was significantly different from zero?
Many thanks for your help!
Tudor