Dear SlicerSALT team,
We are analyzing parkinsonian patients with different cognitive profiles (4 groups in total) and we are interesting in studying shape deformations of subcortical structures between these groups. We were using shapeAnalysisMANCOVA but then we discovered SlicerSALT and the Covariate Significance Testing module. Hence, our first question: why is this module more “efficient”, more “appropriate” than the older one?
We managed to get p-values, etc. from Covariate Significance Testing analysis. However, it is not clear what this analysis does… What are the statistical tests applied? In shapeAnalysisMANCOVA, it was quite clear and one could choose between Wilks Test, Hotelling Test, etc. and it was a permutation method.
Moreover, there are many different p-values (we have 4 covariates (Center, Sex, Age, Education), thus 5 different p-values (i.e. pvalue_Group Center Sex Age Education, pvalue_1, pvalue_2, etc.). What are they related to? How should we interpret these?
Finally, with the older shapeAnalysisMANCOVA analysis, we got deformation vectors which allowed us to know the direction of the deformation cluster (inward or outward). Is it possible to get the same vectors in your new tool?
Thank you for your reply,