Imagine collecting data in two locations at three points in time. The same 500 or so people are interviewed repeatedly, a year apart, in each place. There are many things being asked but of interest to me right now is a binary Yes/No answer to one particular question.
I want to know whether the rate of Yes answers in each location increases over time and whether that increase (if it exists) differs between the locations. A secondary question is whether the two locations differ overall.
For purposes of discussion, let's imagine the question to be "Do you shop at Walmart?" and the two locations being one urban area and one out in the suburbs. The actual data is slightly different but along those lines.
I am almost completely inexperienced with Generalized Estimating Equations but from what I've been reading this is sort of classic example of the population average effects that GEE's are designed to produce. Is that a correct idea?
That's my question, what follows is the technique I'm more familiar with and will use if the GEE approach is not appropriate...
When I use a random-intercepts mixed model (because of the repeated measures on each subject) to get time-by-group estimates of proportion Yes answers, I have to use a method of standardizing two estimates back to the entire study population. [see https://academic.oup.com/ije/article/43/3/962/763470 for details]
If I understand these things correctly, a GLMM like that with the random intercepts is meant to offer subject-level estimation, which is why a marginal standardization technique is required to convert everything back to the population level.