I'm still pretty new to linear mixed models, so any help is highly appreciated.
In my experiment, a test group (gets the intervention) and a control group (does not get the intervention) are observed over time. There are five measurement points (one might think of them as "waves of measurement") for each participant. Within a given measurement point the date one participant fills out her questionnaire might differ from the date for a another participant (i.e. one particpant fills out her questionnaire for measurement point #4 on 2019-02-01 while another particpant fills out her questionnaire for measurement point #4 on 2019-02-20).
I'm interested in the effect of the intervention and the effect of time on some outcome variable.
Based on the things I've read so far, I assume a "naive model" (random slope only, no interaction between fixed effects) in lme4 might look like this:
outcome ~ group + measurement_point + (1|subject)
Now, I've some general questions on this:
- Is the naive model above reasonable?
- What is the best way to model the measurement points (i.e., as individual dates on which participants filled out the questionnaire or as a factor variable representing the number of "waves", e.g. going from 1 to 5)?
- Should I care about missing data assuming that it is missing at random? I have up to 19% of missing values for some measurement points. What's the best way to handle this?