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dc.contributor.authorCherlin, Svetlana
dc.contributor.authorWason, James
dc.date.accessioned2022-06-21T23:31:25Z
dc.date.available2022-06-21T23:31:25Z
dc.date.issued2020-10-30
dc.identifier.issn0277-6715
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338287
dc.description.abstractThere is the potential for high-dimensional information about patients collected in clinical trials (such as genomic, imaging, and data from wearable technologies) to be informative for the efficacy of a new treatment in situations where only a subset of patients benefits from the treatment. The adaptive signature design (ASD) method has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using genetic data. The method requires selection of three tuning parameters which may be highly computationally expensive. We propose a variation to the ASD method, the cross-validated risk scores (CVRS) design method, that does not require selection of any tuning parameters. The method is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure. We assess the properties of CVRS against the originally proposed cross-validated ASD using simulation data and a real psychiatry trial. CVRS, as assessed for various sample sizes and response rates, has a substantial reduction in the computational time required. In many simulation scenarios, there is a substantial improvement in the ability to correctly identify the sensitive group and the power of the design to detect a treatment effect in the sensitive group. We illustrate the application of the CVRS method on the psychiatry trial.
dc.format.mediumPrint-Electronic
dc.publisherWiley
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectadaptive design
dc.subjectclinical trials
dc.subjectrisk scores
dc.subjectsubgroup analysis
dc.subjectComputer Simulation
dc.subjectGenomics
dc.subjectHumans
dc.subjectResearch Design
dc.subjectRisk Factors
dc.subjectSample Size
dc.titleDeveloping and testing high-efficacy patient subgroups within a clinical trial using risk scores.
dc.typeArticle
dc.publisher.departmentMrc Biostatistics Unit
dc.date.updated2022-06-21T11:11:21Z
prism.endingPage3298
prism.issueIdentifier24
prism.publicationDate2020
prism.publicationNameStat Med
prism.startingPage3285
prism.volume39
dc.identifier.doi10.17863/CAM.85695
dcterms.dateAccepted2020-05-28
rioxxterms.versionofrecord10.1002/sim.8665
rioxxterms.versionVoR
dc.contributor.orcidCherlin, Svetlana [0000-0001-9689-3699]
dc.contributor.orcidWason, James [0000-0002-4691-126X]
dc.identifier.eissn1097-0258
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMedical Research Council (MR/N028171/1)
cam.issuedOnline2020-07-14
cam.depositDate2022-06-21
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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