# Explaining a Mixed Effect Model to a Non Statistician/Mathematician

I'm not a statistician, but I do have a basic understanding of biostatistics in the context of medicine and clinical trials. However, recently I came across a trial that is using a statistical method that I am very unfamiliar with and was hoping someone could help.

Here is the study. It's in the "Statistical Analysis" of Clark, D., et al., (2021). Clinically relevant activity of the novel rasp inhibitor reproxalap in allergic conjunctivitis: the Phase 3 ALLEVIATE trial. American Journal of Ophthalmology, 230, 60-67.

Where I'm getting very confused is with this sentence: "Reproxalap was compared to vehicle via a MIXED EFFECT MODEL for Repeated Measures with baseline area under the curve as a covariate and treatment group and minutes post-challenge as factors. A generalized estimating equation procedure, with baseline area under the curve as a covariate and treatment group and minutes post-challenge as factors, was used to compare responder proportions for the key secondary endpoint."

I'm trying to understand what the terms in this entire paragraph actually mean and why this would be a valid statistical method to use for this given trial.

If someone could explain, non-mathematically, the actual intuition and reasoning behind what a "mixed effect model for repeated measures" is(and what the significance of the covariate and factors are in this model) with some examples or point to some sources that explain it well to someone with a basic understanding of stats, and even perhaps then proceed to explain it mathematically, I'd be very grateful.