Some recommendations for multi-arm multi-stage trials

Abstract

Multi-arm multi-stage designs can improve the efficiency of the drug-development process by evaluating multiple experimental arms against a common control within one trial. This reduces the number of patients required compared to a series of trials testing each experimental arm separately against control. By allowing for multiple stages experimental treatments can be eliminated early from the study if they are unlikely to be significantly better than control. Using the TAILoR trial as a motivating example, we explore a broad range of statistical issues related to multi-arm multi-stage trials including a comparison of different ways to power a multi-arm multi-stage trial; choosing the allocation ratio to the control group compared to other experimental arms; the consequences of adding additional experimental arms during a multi-arm multi-stage trial, and how one might control the type-I error rate when this is necessary; and modifying the stopping boundaries of a multi-arm multi-stage design to account for unknown variance in the treatment outcome. Multi-arm multi-stage trials represent a large financial investment, and so considering their design carefully is important to ensure efficiency and that they have a good chance of succeeding.

Keywords: Clinical trial design; group-sequential designs; interim analysis; multi-arm multi-stage designs; multiple-testing; statistical design.

Figure 1.

Maximum sample size and maximum cost (arbitrary units) of treatment as allocation ratio changes. Designs are chosen using triangular stopping boundaries such that they give 5% type-I error and 90% power. Maximum cost assumes that the cost of allocating a patient to the control group is c, and the cost of allocating a patient to an experimental treatment is 1 where c ∈ {1, 0.5, 0.25, 0.1}.