Evidence map›Paper›PMID 39711026›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2025

A Preplanned Multi-Stage Platform Trial for Discovering Multiple Superior Treatments With Control of FWER and Power.

Peter Greenstreet, Thomas Jaki, Alun Bedding, Pavel Mozgunov

Abstract read
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Peter GreenstreetDepartment of Mathematics and Statistics, Lancaster University, Lancaster, UK.ORCID 0000-0002-4997-8742
Thomas JakiMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Alun BeddingData and Statistical Sciences Department, Roche Products Ltd., Welwyn Garden City, UK.
Pavel MozgunovMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Funding

Engineering and Physical Sciences Research Council EP/S022252/1Medical Research Council MC_UU_00002/14Medical Research Council MC_UU_00002/19Medical Research Council MC_UU_00040/03National Institute for Health Research NIHR300576
6 · The paper itself

Abstract

There is a growing interest in the implementation of platform trials, which provide the flexibility to incorporate new treatment arms during the trial and the ability to halt treatments early based on lack of benefit or observed superiority. In such trials, it can be important to ensure that error rates are controlled. This paper introduces a multi-stage design that enables the addition of new treatment arms, at any point, in a preplanned manner within a platform trial, while still maintaining control over the family-wise error rate. This paper focuses on finding the required sample size to achieve a desired level of statistical power when treatments are continued to be tested even after a superior treatment has already been found. This may be of interest if there are treatments from different sponsors which are also superior to the current control or multiple doses being tested. The calculations to determine the expected sample size is given. A motivating trial is presented in which the sample size of different configurations is studied. In addition, the approach is compared to running multiple separate trials and it is shown that in many scenarios if family-wise error rate control is needed there may not be benefit in using a platform trial when comparing the sample size of the trial.

Indexed as

BiometryClinical Trials as TopicAdaptive Clinical Trials as TopicHumansSample SizeFWERmulti‐armmulti‐stageplatform trialspreplanned

Identifiers

PMID39711026
PMCPMC11664203

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.