Note: This article is one of a series about adaptive design that come from a blog written by Dr. Karen Kesler from 2010 to 2011.  That blog is no longer active, but it contained some great information, so we wanted to re-post it here.

I think that pruning designs get a bad rap. Sure, you can’t control Type I error if you’re looking at the data frequently and getting rid of treatment groups in the middle of the study, but there’s a place for this type of design in finding your best dose. Before I get too far ahead of myself, recall that in a pruning study, you start with a bunch of doses or regimens of your active compound and one arm of “placebo”. The placebo could be an active drug, but the goal here is to show superiority, not non-inferiority. You then set up boundaries—both efficacy and futility—for use in multiple interim analyses, with the goal of eliminating (“pruning”) the less effective or less safe doses early in the study. At each of the interim analyses, you calculate the test statistic for comparing each active arm to the placebo arm and see how it compares to your boundaries. Test statistic below the futility boundary? Prune that arm! Test statistic still in the middle of your boundaries? Keep randomizing subjects to that arm. Test statistic above the efficacy boundary? Think about stopping your study. Theoretically, at the end of the study, you’re down to a placebo arm and 1 or 2 active arms—the ones you now want to take to your confirmatory Phase III trial. If you start with 4-6 active dose/regimens and at least 2 interim analyses, you can see that the number of hypothesis tests really starts stacking up. Suppose you have 5 active arms and 3 interim analyses—that’s potentially 15 hypothesis tests before you even get to your final analysis! Of course, if you really have 15 tests, the design isn’t working right. You should be eliminating 1-2 arms in each interim analysis.