Good day everyone,

I'm a beginning ophthalmology researcher/student, and I wanted to reach out people here to learn about mixed effects models and generalized estimating equations. I've tried to read some sources and ask around my peers, but they use different statistical software and I haven't been too confident to understand this topic yet. My problem is that in my field there are 2 eyes in 1 patient, so the observations are not fully independent from each other, or are nested data. In my field I think it is standard to adjust for this factor, but I have not been very confident at all in how to do this. I was given advice that I will probably need to use the command VCE and XTREG, so I've read about those commands but still not confident to use them. I had the following questions:
1) Is there any published material/videos you could point me to so I could know how to implement this in my work? Perhaps I am not reading the correct materials and a recommendation from people here could help. I've done a few research projects using stata before, so I am intermediate-beginner level and pretty much still learning the ropes.
2) Usually in medical research, we have a chart comparing the differences in baseline characteristics of patients, then afterwards the different analysis usually regression, anova, kaplan meier curves, comparison of area under the curves, etc. Does adjusting using the mixed effect models/GEE affect all the analysis in these data by fundamentally changing how the dataset is arranged? And then I can just run these analysis the standard way. Or do I have to place an additional command when running each analysis so that it becomes adjusted?
3) I dont know if it is possible, but perhaps the easiest way to learn this is if there is an internet source that had an illustrative nested dataset, then perhaps a do-file that shows how to apply the mixed effect models/GEEs to statistical analysis.

Thank you again for any help on this matter, as I did not know anywhere else to turn to. People at my school weren't too helpful I thought it best to reach out here.

Best,
Cris J.