# Paired or not paired? Comparing groups after propensity score matching

After matching on propensity score, e.g 1:1 matching, you obtain a matched subset of your data.

The built-in functions in the Matching package, as a prominent example, compares groups before matching by use of non-paired t-test but then switches to the paired t-test to compare groups after matching.

Publications, in medical journals at least, obviously fail to report what type of t-test they have chosen. As far as I can tell, I haven't seen a single one using the paired test, which contrasts rather strongly against J Sekhons Matching package.

I "did like the rest" and used a non-paired t-test to compare continuous variables in a propensity score matched cohort (1:1 matched).

• I think they don't say because they think it's obvious that cases are now matched so you use a paired $t$. I suppose the independent-sample test is a conservative approach (so not exactly wrong), where you only use the propensity scores to ensure that you have comparable populations. I'll be interested to see what the propensity-score experts say. – Russ Lenth Dec 23 '14 at 23:47
• yes, it might be so obvious it isn't spelled out. But I'm afraid that many published baseline tables (which are extremely important in this scenario) are simply based on independent samples t test; particularly since these tests are easily performed with the tableone package. Where's our experts on Christmas? :-) – Adam Robinsson Dec 24 '14 at 16:26
• I did find a related post - stats.stackexchange.com/questions/25392/… - that includes a quote from a reference that recommends an independent-samples test. "No reason to believe they are correlated just because they share a propensity score." – Russ Lenth Dec 26 '14 at 2:33