A variable is measured repeatedly within a study (e. g. blood sugar levels every day over the course of two weeks while patients are giving a specific treatment). There are also two follow-up measurements three and six months later, they do not include any treatment and are just to determine if effects are still visible after a few months.
Do I need to take into account that the first 14 measurements were taken on consecutive days (and within a short time frame) but the follow-ups were conducted vastly later? My model looks like this
lme(level ~ treatment * session, data, random = ~ 1 + session | ID, method = "REML"))
and I am interested in how the blood sugar levels change over time depending on specific treatments and whether the changes are still measurable during follow-ups.
In this case, session would now include numbers (from 1 to 16) for each measurement point. This way, there is no indication how far the measurement points are away from each other. Would this be necessary?