I am facing problem in deciding the right analysis to answer my question.
I have 98 subjects. Each subject was measured 3 times (Baseline Year, Year 1, Year 2) on treatment efficacy marker Albumin and they were nested in site locations. There were two types of treatment or access type- AVF & AVG. Initially, subjects were given an Old treatment "Ot". However, due to some complications from Ot (indicated by a decrease in Albumin. An increase is considered to be an improvement), subjects were assigned to AVF & AVG. This assignment was not randomized.
After collecting the baseline info for each subject, they were given either AVF or AVG. These two treatments were assigned after the baseline year.
87 subjects got AVF and 11 subjects got AVG. There were missing values on the repeated measurements. Missing albumin measurement were more frequent in year 2 (46%) followed by year 1 (13%) and baseline year (6%).
My research question- what is the effect of treatment type on the efficacy marker over time.
I have read that LMM can handle the missing values. Also, it can account for the variation in nesting. So, what is your suggestion about applying LMM technique here.
If I use LMM, I believe that I need to consider time as categorical. Then according to Randel's answer, I need to consider only the random intercept. Not the random slope. But why?