Evidence map›Paper›PMID 31937351›Full record

Trial reportTrials2020

Bayesian group sequential designs for phase III emergency medicine trials: a case study using the PARAMEDIC2 trial.

Elizabeth G Ryan, Nigel Stallard, Ranjit Lall, Chen Ji, Gavin D Perkins, Simon Gates

Open access · goldAbstract readClinical Trial, Phase IIIRandomized 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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.2field-weighted citation impact, top 11% 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

7 citing papers in PubMed, 15 citations in OpenAlex.

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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

6 authors at 2 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
Nigel StallardStatistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Ranjit LallWarwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Chen JiWarwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Gavin D PerkinsWarwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Simon GatesCancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
University of Warwick · GBUniversity Hospitals Birmingham NHS Foundation Trust · GB

Funding

Cancer Research UK 25354Department of Health 12/127/126Medical Research Council MR/N028287/1Medical Research Council MR/N028287/2
6 · The paper itself

Abstract

backgroundPhase III trials often require large sample sizes, leading to high costs and delays in clinical decision-making. Group sequential designs can improve trial efficiency by allowing for early stopping for efficacy and/or futility and thus may decrease the sample size, trial duration and associated costs. Bayesian approaches may offer additional benefits by incorporating previous information into the analyses and using decision criteria that are more practically relevant than those used in frequentist approaches. Frequentist group sequential designs have often been used for phase III studies, but the use of Bayesian group sequential designs is less common. The aim of this work was to explore how Bayesian group sequential designs could be constructed for phase III trials conducted in emergency medicine.

methodsThe PARAMEDIC2 trial was a phase III randomised controlled trial that compared the use of adrenaline to placebo in out-of-hospital cardiac arrest patients on 30-day survival rates. It used a frequentist group sequential design to allow early stopping for efficacy or harm. We constructed several alternative Bayesian group sequential designs and studied their operating characteristics via simulation. We then virtually re-executed the trial by applying the Bayesian designs to the PARAMEDIC2 data to demonstrate what might have happened if these designs had been used in practice.

resultsWe produced three alternative Bayesian group sequential designs, each of which had greater than 90% power to detect the target treatment effect. A Bayesian design which performed interim analyses every 500 patients recruited produced the lowest average sample size. Using the alternative designs, the PARAMEDIC2 trial could have declared adrenaline superior for 30-day survival with approximately 1500 fewer patients.

conclusionsUsing the PARAMEDIC2 trial as a case study, we demonstrated how Bayesian group sequential designs can be constructed for phase III emergency medicine trials. The Bayesian framework enabled us to obtain efficient designs using decision criteria based on the probability of benefit or harm. It also enabled us to incorporate information from previous studies on the treatment effect via the prior distributions. We recommend the wider use of Bayesian approaches in phase III clinical trials.

trial registrationPARAMEDIC2 Trial registration ISRCTN, ISRCTN73485024. Registered 13 March 2014, http://www.isrctn.com/ISRCTN73485024.

Indexed as

Bayes TheoremCase-Control StudiesClinical Decision-MakingClinical Trials Data Monitoring CommitteesComputer SimulationEmergency MedicineEpinephrineHumansOut-of-Hospital Cardiac ArrestPlacebosResearch DesignSample SizeSurvival RateSympathomimeticsEpinephrinePlacebosSympathomimeticsBayesian design, Group sequential design, Interim analysis, Cardiac arrest, Randomised controlled trials, Monitoring, Phase III

Identifiers

PMID31937351
PMCPMC6961266
OpenAlexW3000888732

What OpenQuestion holds

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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.