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dc.contributor.authorOuma, Luke Ondijo
dc.contributor.authorGrayling, Michael J
dc.contributor.authorZheng, Haiyan
dc.contributor.authorWason, James
dc.date.accessioned2021-11-22T14:47:53Z
dc.date.available2021-11-22T14:47:53Z
dc.date.issued2021-11
dc.date.submitted2020-08-28
dc.identifier.issn1539-1604
dc.identifier.otherpst2119
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330912
dc.description.abstractUmbrella trials are an innovative trial design where different treatments are matched with subtypes of a disease, with the matching typically based on a set of biomarkers. Consequently, when patients can be positive for more than one biomarker, they may be eligible for multiple treatment arms. In practice, different approaches could be applied to allocate patients who are positive for multiple biomarkers to treatments. However, to date there has been little exploration of how these approaches compare statistically. We conduct a simulation study to compare five approaches to handling treatment allocation in the presence of multiple biomarkers - equal randomisation; randomisation with fixed probability of allocation to control; Bayesian adaptive randomisation (BAR); constrained randomisation; and hierarchy of biomarkers. We evaluate these approaches under different scenarios in the context of a hypothetical phase II biomarker-guided umbrella trial. We define the pairings representing the pre-trial expectations on efficacy as linked pairs, and the other biomarker-treatment pairings as unlinked. The hierarchy and BAR approaches have the highest power to detect a treatment-biomarker linked interaction. However, the hierarchy procedure performs poorly if the pre-specified treatment-biomarker pairings are incorrect. The BAR method allocates a higher proportion of patients who are positive for multiple biomarkers to promising treatments when an unlinked interaction is present. In most scenarios, the constrained randomisation approach best balances allocation to all treatment arms. Pre-specification of an approach to deal with treatment allocation in the presence of multiple biomarkers is important, especially when overlapping subgroups are likely.
dc.languageen
dc.publisherWiley
dc.subjectMAIN PAPER
dc.subjectMAIN PAPERS
dc.subjectadaptive design
dc.subjectadaptive randomisation
dc.subjectconstrained randomisation
dc.subjectpatient allocation
dc.subjectprecision medicine
dc.subjectstratified randomisation
dc.titleTreatment allocation strategies for umbrella trials in the presence of multiple biomarkers: A comparison of methods.
dc.typeArticle
dc.date.updated2021-11-22T14:47:52Z
prism.endingPage1001
prism.issueIdentifier6
prism.publicationNamePharm Stat
prism.startingPage990
prism.volume20
dc.identifier.doi10.17863/CAM.78355
dcterms.dateAccepted2021-03-13
rioxxterms.versionofrecord10.1002/pst.2119
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidOuma, Luke Ondijo [0000-0001-9145-8719]
dc.contributor.orcidZheng, Haiyan [0000-0002-3385-2117]
dc.contributor.orcidWason, James [0000-0002-4691-126X]
dc.identifier.eissn1539-1612
pubs.funder-project-idMedical Research Council (MR/N028171/1)
pubs.funder-project-idMedical Research Council (MC_UU_00002/14)
pubs.funder-project-idMedical Research Council (MR/S014357/1)
cam.issuedOnline2021-03-24


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