I wish to examine the effect of an intervention on a set of ~60 mutually correlated features (dependent variables), measured at baseline and post-intervention in ~70 subjects. At both baseline and follow-up each feature was measured under two different conditions. I want to determine if which features were significantly effected by the intervention.
I don't believe a repeated measures ANOVA is appropriate due to the large number of mutually correlated features. I also don't believe multiple t-tests are acceptable due to the risk of Type I error (particularly at a 0.05 significance level).
Could using the change scores (i.e. change in each feature between baseline and follow-up) in a Lasso regression be a valid method?
What is the most appropriate statistical approach? Grateful for any help.