I have a longitudinal dataset where patients have a measurement with a date, currently coded as time from end of treatment (days). Now, I want to build a model. Roughly, a zero inflated Poisson model seems to fit outcome best (patients score a number of symptoms, or DS).
To build the model, I use glmmADMB in R. My formula then looks like this:
DS ~ PF2 + EF + QL2 + HNWG + (1|Patient)
Time since treatment could be influencing the outcome.
Does it make sense to include time as a (nested) random effect? Should I include it as a fixed effect?
To get a bit more insight I made a plot. Aside from a slightly elevated start, time does not seem that important, am I correct in my interpretation? Is this enough information? If not, what extra steps should I execute to find out what I should do?