I have 3 measurement points, N=4000, N=2000, N=3000 (week 0, week 6, week 12).
My goal is to estimate the relative importance of 14 regressors on one dependent variable. Now I can do that separately for each time point, but the N are comparably small to estimate relative regressor importance with such a huge number of regressors.
So I thought about simply "pooling" participants, pretending the data are cross-sectional, and using N=4000+2000+3000. Obviously this violates the assumption of independence of observations, so I should not do that.
Alternatives? I can't use a regression in which I simply add time as regressor because the bottleneck week 6 (only N=2000) will lead to listwise deletion of 50% of the subjects.