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dc.contributor.authorEwings, Sean
dc.contributor.authorSaunders, Geoff
dc.contributor.authorJaki, Thomas
dc.contributor.authorMozgunov, Pavel
dc.date.accessioned2022-02-22T02:01:59Z
dc.date.available2022-02-22T02:01:59Z
dc.date.issued2022-01-20
dc.identifier.issn1471-2288
dc.identifier.otherPMC8771176
dc.identifier.other35057758
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334290
dc.description.abstractBACKGROUND: Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge. METHODS: We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments. RESULTS: We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs. CONCLUSIONS: This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 100968545
dc.sourceessn: 1471-2288
dc.subjectPhase I
dc.subjectBayesian
dc.subjectAdaptive Design
dc.subjectDose Escalation
dc.subjectHumans
dc.subjectBayes Theorem
dc.subjectMaximum Tolerated Dose
dc.subjectDose-Response Relationship, Drug
dc.subjectResearch Design
dc.subjectPandemics
dc.subjectCOVID-19
dc.subjectSARS-CoV-2
dc.titlePractical recommendations for implementing a Bayesian adaptive phase I design during a pandemic.
dc.typeArticle
dc.date.updated2022-02-22T02:01:56Z
prism.issueIdentifier1
prism.publicationNameBMC Med Res Methodol
prism.volume22
dc.identifier.doi10.17863/CAM.81703
dcterms.dateAccepted2022-01-06
rioxxterms.versionofrecord10.1186/s12874-022-01512-0
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidJaki, Thomas [0000-0002-1096-188X]
dc.identifier.eissn1471-2288
pubs.funder-project-idMedical Research Council (MC_UU_00002/14)
pubs.funder-project-idNIHR Academy (NIHRDH-SRF-2015-08-001)
pubs.funder-project-idMedical Research Council (MR/V028391/1)
pubs.funder-project-idNational Institute for Health Research (NIHRDH-IS-BRC-1215-20014)
cam.issuedOnline2022-01-20


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International