I have a small dataset with 7 subjects only.
Each subject got a treatment for a disease. Before getting the treatment, some measure of the disease was taken, let's call it "outcome". This is a continuous variable.
After the treatment, the "outcome" was measures several more times (1 day after, 1 week after, 1 month after).
I need to analyze this data, to see if there are differences after the treatment. I understand that a full longitudinal analysis is not feasible because I have N=7, and there is not enough power.
I thought to make specific comparisons, i.e. paired t-test (or non-parametric equivalent) per time point, for example, to compare 1 week to baseline, and then 1 months to baseline, and so on.
My question is, should I analyze the absolute change from baseline, or the percentage change from baseline? If this was a longitudinal model, the baseline would have been a covariate. But with a simple test, the value of the baseline can vary from one subject to another.
Should I take outcome - baseline or (outcome-baseline)/baseline? In the latter case, how exactly do I perform a paired t-test? Do you have a better suggestion?
My data contains the following variables: ID, time (baseline, 1 day, 1 week, 1 month), outcome.