Evidence map›Paper›PMID 31937341›Full record

Trial reportTrials2020

Bayesian adaptive designs for multi-arm trials: an orthopaedic case study.

Elizabeth G Ryan, Sarah E Lamb, Esther Williamson, Simon Gates

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Trials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
4.6field-weighted citation impact, top 4% of its field
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

21 citing papers in PubMed, 35 citations in OpenAlex.

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  10. A practical guide to adopting Bayesian analyses in clinical research.Journal of clinical and translational science · 2024
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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 at 3 institutions in 1 country.

Elizabeth G RyanCancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK. E.G.Ryan@bham.ac.uk.ORCID http://orcid.org/0000-0001-9367-4204
Sarah E LambCentre for Rehabilitation Research, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK.
Esther WilliamsonCentre for Rehabilitation Research, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK.
Simon GatesCancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
Cancer Research UK Clinical Trials Unit · GBNuffield Orthopaedic Centre · GBUniversity of Exeter · GB

Funding

Cancer Research UK 25354Medical Research Council MR/N028287/1Medical Research Council MR/N028287/2
6 · The paper itself

Abstract

backgroundBayesian adaptive designs can be more efficient than traditional methods for multi-arm randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive designs can be constructed for multi-arm phase III clinical trials and assess potential benefits that these designs offer.

methodsWe constructed several alternative Bayesian adaptive designs for the Collaborative Ankle Support Trial (CAST), which was a randomised controlled trial that compared four treatments for severe ankle sprain. These designs incorporated response adaptive randomisation (RAR), arm dropping, and early stopping for efficacy or futility. We studied the operating characteristics of the Bayesian designs via simulation. We then virtually re-executed the trial by implementing the Bayesian adaptive designs using patient data sampled from the CAST study to demonstrate the practical applicability of the designs.

resultsWe constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original designs target sample size. The virtual executions showed that most of the Bayesian designs would have led to trials that declared superiority of one of the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more likely to allocate patients to better performing arms at each interim analysis. Similar estimates and conclusions were obtained from the Bayesian adaptive designs as from the original trial.

conclusionsUsing CAST as an example, this case study shows how Bayesian adaptive designs can be constructed for phase III multi-arm trials using clinically relevant decision criteria. These designs demonstrated that they can potentially generate earlier results and allocate more patients to better performing arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.

trial registrationCAST study registration ISRCTN, ISRCTN37807450. Retrospectively registered on 25 April 2003.

Indexed as

Ankle InjuriesBayes TheoremBracesClinical Trials, Phase III as TopicComparative Effectiveness ResearchComputer SimulationDecision MakingEmergency MedicineEnglandHumansOrthopedicsRecovery of FunctionResearch DesignTrauma Severity IndicesArm droppingBayesian adaptive designEmergency medicineInterim analysisMonitoringMulti-arm trialOrthopaedicPhase IIIRandomised controlled trialsResponse adaptive randomisation

Identifiers

PMID31937341
PMCPMC6961269
OpenAlexW3001528581

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LicenceCC BY
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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.