I have a statistical project that am working on that is made up of two groups(control and intervention) and the participants in the two groups are different from each other. measurement of biomarkers were taken at baseline and endpoint for the intervention group within 12 weeks.i.e, patient P1 in control group body weight was taken at baseline and after 12weeks. same process for another patient in the intervention group. in the end, there are about 300 variables for 21 participants.
I want to know if there is any difference within and between the two groups.I mean, what is the effect of the intervention within and between groups? Initially, 'GLMM' was proposed.but from the data, it did not meet the assumptions of GLMM. I intend to use wilcoxon signed rank test .again, it seems it is not the right statistical model to apply because I assumed the measurements(data) are independent between groups and to answer the research question.I also thought of Linear mixed model(LMM) but am not quite sure looking at the assumption of LMM. my first approach is to apply PCA/K-means to identify clusters of variables, then use these clusters variables for analysis.
NB: all the data are continuous
I need suggestions/help on the right approach(model) to use for this analysis.
The attached images is the design of the experiment
