Abstract
BackgroundThe multi-arm multi-stage framework uses intermediate outcomes to assess lack-of-benefit of research arms at interim stages in randomised trials with time-to-event outcomes. However, the design lacks formal methods to evaluate early evidence of overwhelming efficacy on the definitive outcome measure. We explore the operating characteristics of this extension to the multi-arm multi-stage design and how to control the pairwise and familywise type I error rate. Using real examples and the updated nstage program, we demonstrate how such a design can be developed in practice.MethodsWe used the Dunnett approach for assessing treatment arms when conducting comprehensive simulation studies to evaluate the familywise error rate, with and without interim efficacy looks on the definitive outcome measure, at the same time as the planned lack-of-benefit interim analyses on the intermediate outcome measure. We studied the effect of the timing of interim analyses, allocation ratio, lack-of-benefit boundaries, efficacy rule, number of stages and research arms on the operating characteristics of the design when efficacy stopping boundaries are incorporated. Methods for controlling the familywise error rate with efficacy looks were also addressed.ResultsIncorporating Haybittle–Peto stopping boundaries on the definitive outcome at the interim analyses will not inflate the familywise error rate in a multi-arm design with two stages. However, this rule is conservative; in general, more liberal stopping boundaries can be used with minimal impact on the familywise error rate. Efficacy bounds in trials with three or more stages using an intermediate outcome may inflate the familywise error rate, but we show how to maintain strong control.ConclusionThe multi-arm multi-stage design allows stopping for both lack-of-benefit on the intermediate outcome and efficacy on the definitive outcome at the interim stages. We provide guidelines on how to control the familywise error rate when efficacy boundaries are implemented in practice.
Highlights
The multi-arm multi-stage (MAMS) adaptive clinical trial design developed by Royston et al.[1,2] has many practical advantages when evaluating treatments, such as increased efficiencies in time and patients required, and a greater probability of success than a traditional parallel-group, single-stage design.[3]
Our simulations indicate that in a twoarm two-stage design the inclusion of the Haybittle–Peto efficacy rule at the interim stage has a minimal impact on the pairwise error rate (PWER) under any configuration of the timing of interim analysis, the value of the final-stage significance level and the design allocation ratio
The extent of inflation of the familywise error rate (FWER) is determined by the choice of efficacy stopping boundary and whether an intermediate outcome is used
Summary
The multi-arm multi-stage (MAMS) adaptive clinical trial design developed by Royston et al.[1,2] has many practical advantages when evaluating treatments, such as increased efficiencies in time and patients required, and a greater probability of success than a traditional parallel-group, single-stage design.[3]. The multi-arm multi-stage framework uses intermediate outcomes to assess lack-of-benefit of research arms at interim stages in randomised trials with time-to-event outcomes. Methods: We used the Dunnett approach for assessing treatment arms when conducting comprehensive simulation studies to evaluate the familywise error rate, with and without interim efficacy looks on the definitive outcome measure, at the same time as the planned lack-of-benefit interim analyses on the intermediate outcome measure. We studied the effect of the timing of interim analyses, allocation ratio, lack-of-benefit boundaries, efficacy rule, number of stages and research arms on the operating characteristics of the design when efficacy stopping boundaries are incorporated. Results: Incorporating Haybittle–Peto stopping boundaries on the definitive outcome at the interim analyses will not inflate the familywise error rate in a multi-arm design with two stages. We provide guidelines on how to control the familywise error rate when efficacy boundaries are implemented in practice
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