# Testing for paired subjects AND two timepoints

How should I best analyze data that have "2x pairing"? E.g., N set of twins, each twin randomly assigned to active drug or placebo, then measure indicator X at timepoints A (before administration) and B (after administration).

My understanding here is that there are 2 pairings: between time A and B, and between drug and placebo.

Does it make sense to manually calculate the difference in X between the timepoints and just perform a simple paired t-test / Wilcoxon on the differences?

If that's OK, then is there a difference if I do it the other way around, i.e. manually calculate the difference in X between drug and placebo, and perform a simple paired test across the timepoints? (It feels less logical, but I can't place why exactly)

So your suggestion to measure change in $X$ across the trial period (i.e. $X_{\text{B}}-X_{\text{A}}$) and base your inference on a paired t test or sign-rank test, would seem to be about right.
If you based your inference on $X_{\text{sib drug}}-X_{\text{sib control}}$, and then performed your test (paired t or signed rank), you would then be inferring that there was a change (or not) in drug-placebo differences in prevalence/levels before and after treatment. Possibly also useful. But aren't clinical trials more often used to characterize effects on risk/change?
• Sorry, I didn't know [enter] submitted the comment. Yup, the main purpose is to compare difference between drug and placebo. I guess what I meant was, is there a difference if I calculate X_B - X_A then perform a paired test across X_drug and X_ctrl, vs. calculating X_drug - X_ctrl then performing a paired test across X_B and X_A? Both methods seem to account for the differences between time period and drug/control... does it matter which particular pair is operated on by the statistical test?