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dc.contributor.authorBennett, Maxine
dc.contributor.authorWhite, Simon
dc.contributor.authorBest, Nicky
dc.contributor.authorMander, Adrian
dc.date.accessioned2021-04-28T08:11:48Z
dc.date.available2021-04-28T08:11:48Z
dc.date.issued2021-01-20
dc.date.submitted2019-02-14
dc.identifier.issn1539-1604
dc.identifier.issn1539-1612
dc.identifier.otherpst2088
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/321677
dc.descriptionFunder: National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre
dc.description.abstractAbstract: A standard two‐arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down‐weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error.
dc.languageen
dc.publisherJohn Wiley & Sons, Inc.
dc.subjectMAIN PAPER
dc.subjectMAIN PAPERS
dc.subjectadaptive design
dc.subjectBayesian
dc.subjectborrowing
dc.subjecthistorical data
dc.subjectpriors
dc.titleA novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods
dc.typeArticle
dc.date.updated2021-04-28T08:11:47Z
prism.endingPage484
prism.issueIdentifier3
prism.publicationNamePharmaceutical Statistics
prism.startingPage462
prism.volume20
dc.identifier.doi10.17863/CAM.68796
dcterms.dateAccepted2020-10-14
rioxxterms.versionofrecord10.1002/pst.2088
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBennett, Maxine [0000-0001-6298-519X]
dc.contributor.orcidWhite, Simon [0000-0001-8642-7037]
dc.contributor.orcidBest, Nicky [0000-0003-4120-9727]
pubs.funder-project-idUK Medical Research Council (MRC_MC_UU_00002/3, U105292687)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EPSRC) iCase award in conjunction with GlaxoSmithKline (2147518)


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