I am trying to analyse some data and I am a bit lost as to what is the correct approach to take.
The data are blood hormone levels, taken repeatedly from various subjects, after a certain treatment, which is given at 10', after 2 control samples are taken at time 0' and 10'.
So essentially I have some data that looks like this:
time <- c(0, 10, 20, 30, 45, 60) response.1 <- c(8, 10, 66, 70, 30, 12) response.2 <- c(5, 2, 20, 30, 10, 12) response.3 <- c(10, 8, 80, 70, 40, 22)
As you can see the levels are low for the first two control points, then the stimulus is applied, the levels quickly rise and then slowly descend back to baseline over time.
I am a bit stuck as to how to model such a situation, especially what I am not sure how to include in the model is the fact that not all the points are equal in terms of exposure to the stimulus (for the first two points the subject did not have the stimulus at all, and the others are at different points in time after the stimulus).
The ultimate goal of this analysis would be to tell whether the response is the same for all of the subjects or not (depending on other factors that I will, of course, include in my model). In the example above, subject 2 had a very reduced response.