In a classical drop-loser (or drop-arm) design, patients are randomized into all arms (doses) and at the interim analysis, inferior arms are dropped. Therefore, compared to the traditional dose-finding design, this adaptive design can reduce the sample size by not carrying over all doses to the end of the trial or dropping the losers earlier. However, all the doses have to be explored. For unimodal (including linear or umbrella) response curves, we proposed an effective dose-finding design that allows adding arms at the interim analysis. The trial design starts with two arms, depending on the response of the two arms and the unimodality assumption; we can decide which new arms to be added. This design does not require exploring all arms (doses) to find the best responsive dose; therefore, it can further reduce the sample size from the drop-loser design by as much as 10-20.
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