I am running two longitudinal models for two different populations I'd like to compare. N1=4,000 individuals (translated into about 20,000 rows; 18 variables) and N2=400,000 individuals (~4 million rows; 15 variables). Since I am interested in the individual-level inference of my outcome, I have been using GLIMMIX with random intercept in SAS. This works very well for population 1, but not for population 2 (error message: data is too large). I also have tried to work in R, and my model still did not converge. A GEE model, however, runs smoothly for population 2.
It's been a very frustrating experience to work on this large dataset, so my questions are: (1) Do you know if the results of GEE and GLMM would, perhaps, be similar/comparable in such huge dataset? (2) Anyone else has worked with such large data using mixed effects model as well? Anything worked? Thanks.