I want to make sure I'm properly accounting for the mixed effects in my model.
I am measuring characteristics of patient's eye's with disease and without disease, and determining whether a certain measurement is affected by a few features, sometimes at different time points. The measurement's are taken either of the right, left, or sometimes both eyes. There is no significance as to why it might be measured in both eyes for some patients and not others.
Subject Eye Measurement_1 Measurement_2 Time Disease A R 3 3 1990 0 B R 4 2 1990 1 B L 2 1 1990 1 C L 1 3 1990 0 B R 6 4 1991 1
So based on the above, I wanted to control for inter-eye variation via the following:
lmer(Measurement_1 ~ Disease + Measurement_2 + (1|Subject/Eye)
Now, if I only look at subjects in 1990 (as sometimes occurs when I need to run subsets on the data), I run into the error that the number of levels of each grouping factor must be < number of observations. I assume this error is because the Subject:Eye factor is equal to the number of rows when I'm only looking at patients in 1990.
Question #1: Is it appropriate in the above example to control for inter-eye variation simply with the mixed effect term: (1|Subject)? My concern is that there are possibilities for differences within the eye's of a single subject. For example, based on whether they use a treatment drop in one eye more than the other, or whether a disease had manifested more in one eye instead of the other. So would 1|Subject properly account for that?
Question #2: What is the best way to approach accounting for some patients who had repeated measurements at a different in time? Would it be better to include time as a Fixed effect or to include it as a mixed effect variable?