I am analysing a dataset from a randomised controlled trial (2 treatment groups) with measurements at 3 time points (weeks 0, 1 and 8). I am struggling with whether to analyse this with the three time points as a continuous or categorical variable.
My reasoning to classify as continuous would be to account for the differently spaced time periods between the visits. But this would assume the influence of time is a linear one. This becomes a problem for some of the dependent variables that sharply increase from week 0 to week 1, but then decrease from week 1 to week 8.
Would it make more sense to run the model with time as a categorical variable or to include a quadratic time variable and run it as continuous? Both approaches seem to depict the actual observation when plotting the predicted values as a graph.