ArticleTrials2016
Type I error rates of multi-arm multi-stage clinical trials: strong control and impact of intermediate outcomes.
Article in Trials, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Trial
- Developing Interim Outcome Measures for Multi-Arm Multistage Clinical Trials in Multiple Sclerosis.Neurology · 2026Article
- A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.Statistics in medicine · 2026Article
- A visual framework for classifying adaptive design clinical trials using the GATE frame and PICO terminology.Dialogues in health · 2026Review
- Extension of Fisher's least significant difference method to multi-armed group-sequential response-adaptive designs.Statistical methods in medical research · 2025Article
- A Preplanned Multi-Stage Platform Trial for Discovering Multiple Superior Treatments With Control of FWER and Power.Biometrical journal. Biometrische Zeitschrift · 2025Article
- A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial.Clinical trials (London, England) · 2024Article
- Bayesian Optimal Designs for Multi-Arm Multi-Stage Phase II Randomized Clinical Trials with Multiple Endpoints.Statistics in biopharmaceutical research · 2024Article
- Recent innovations in adaptive trial designs: A review of design opportunities in translational research.Journal of clinical and translational science · 2023Review
- Combining factorial and multi-arm multi-stage platform designs to evaluate multiple interventions efficiently.Clinical trials (London, England) · 2022Article
- Bayesian adaptive decision-theoretic designs for multi-arm multi-stage clinical trials.Statistical methods in medical research · 2021Article
- Adding new experimental arms to randomised clinical trials: Impact on error rates.Clinical trials (London, England) · 2020Article
- The future of clinical trials in urological oncology.Nature reviews. Urology · 2019Review
- Assessing the impact of efficacy stopping rules on the error rates under the multi-arm multi-stage framework.Clinical trials (London, England) · 2019Article
- An optimised multi-arm multi-stage clinical trial design for unknown variance.Contemporary clinical trials · 2018Article
- Adaptive designs in clinical trials: why use them, and how to run and report them.BMC medicine · 2018Article
- Testing many treatments within a single protocol over 10 years at MRC Clinical Trials Unit at UCL: Multi-arm, multi-stage platform, umbrella and basket protocols.Clinical trials (London, England) · 2017Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe multi-arm multi-stage (MAMS) design described by Royston et al. [Stat Med. 2003;22(14):2239-56 and Trials. 2011;12:81] can accelerate treatment evaluation by comparing multiple treatments with a control in a single trial and stopping recruitment to arms not showing sufficient promise during the course of the study. To increase efficiency further, interim assessments can be based on an intermediate outcome (I) that is observed earlier than the definitive outcome (D) of the study. Two measures of type I error rate are often of interest in a MAMS trial. Pairwise type I error rate (PWER) is the probability of recommending an ineffective treatment at the end of the study regardless of other experimental arms in the trial. Familywise type I error rate (FWER) is the probability of recommending at least one ineffective treatment and is often of greater interest in a study with more than one experimental arm.
methodsWe demonstrate how to calculate the PWER and FWER when the I and D outcomes in a MAMS design differ. We explore how each measure varies with respect to the underlying treatment effect on I and show how to control the type I error rate under any scenario. We conclude by applying the methods to estimate the maximum type I error rate of an ongoing MAMS study and show how the design might have looked had it controlled the FWER under any scenario.
resultsThe PWER and FWER converge to their maximum values as the effectiveness of the experimental arms on I increases. We show that both measures can be controlled under any scenario by setting the pairwise significance level in the final stage of the study to the target level. In an example, controlling the FWER is shown to increase considerably the size of the trial although it remains substantially more efficient than evaluating each new treatment in separate trials.
conclusionsThe proposed methods allow the PWER and FWER to be controlled in various MAMS designs, potentially increasing the uptake of the MAMS design in practice. The methods are also applicable in cases where the I and D outcomes are identical.
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