Abstract
One family of designs that can noticeably improve efficiency in later stages of drug development are multi-arm multi-stage (MAMS) designs. They allow several arms to be studied concurrently and gain efficiency by dropping poorly performing treatment arms during the trial aswell as by allowing to stop early for benefit. Conventional MAMS designs were developed for the setting, in which treatment arms are independent and hence can be inefficient when an order in the effects of the arms can be assumed (eg,when considering different treatment durations or different doses). In this work, we extend the MAMS framework to incorporate the order of treatment effects when no parametric dose-response or duration-responsemodel is assumed. The design can identify all promising treatments with high probability. We show that the design provides strong control of the family-wise error rate and illustrate the design in a study of symptomatic asthma. Via simulations we show that the inclusion of the ordering information leads to better decision-making compared to a fixed sample and aMAMS design. Specifically, in the considered settings, reductions in sample size of around 15% were achieved in comparison to a conventional MAMS design.
Highlights
Drug development is costly and time consuming.[1]
The aim of the current study was to explore multi-arm multi-stage designs (MAMS) designs that could select the most promising arm associated to the minimum treatment effect
In the proposed approach we claim the effectiveness of the arm associated to the minimum treatment effect compared to the control only if we claim that the treatment associated to the maximum effect is efficacious
Summary
Drug development is costly and time consuming.[1] One family of clinical trial designs that can improve the development process are multi-arm multi-stage designs (MAMS).[2,3,4] In a MAMS trial, insufficiently promising treatments can be dropped or the trial can be stopped due to overwhelming benefit at a series of interim analyses To date these designs have focused on the setting of independent treatment arms and have been argued to be a highly efficient approach to clinical trials.[5,6,7] They could, be suboptimal if an “order” (ie, a monotonic relationship) among the treatment effects can be assumed. In infectious diseases such as Tuberculosis (TB) and Hepatitis B (HBV), the treatment duration with current standard regimes is lengthy[8]
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