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dc.contributor.authorMcMenamin, Martina
dc.contributor.authorGrayling, Michael J
dc.contributor.authorBerglind, Anna
dc.contributor.authorWason, James MS
dc.date.accessioned2021-12-24T14:36:52Z
dc.date.available2021-12-24T14:36:52Z
dc.date.issued2021-12-07
dc.date.submitted2021-01-03
dc.identifier.issn2520-1026
dc.identifier.others41927-021-00224-0
dc.identifier.other224
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331799
dc.descriptionFunder: NIHR Cambridge Biomedical Research Centre
dc.description.abstractBACKGROUND: Composite responder endpoints feature frequently in rheumatology due to the multifaceted nature of many of these conditions. Current analysis methods used to analyse these endpoints discard much of the data used to classify patients as responders and are therefore highly inefficient, resulting in low power. We highlight a novel augmented methodology that uses more of the information available to improve the precision of reported treatment effects. Since these methods are more challenging to implement, we developed free, user-friendly software available in a web-based interface and as R packages. The software consists of two programs: one that supports the analysis of responder endpoints; the second that facilitates sample size estimation. We demonstrate the use of the software to conduct the analysis with both the augmented and standard analysis method using the MUSE study, a phase IIb trial in patients with systemic lupus erythematosus. RESULTS: The software outputs similar point estimates with smaller confidence intervals for the odds ratio, risk ratio and risk difference estimators using the augmented approach. The sample size required in each arm for a future trial using the novel approach based on the MUSE data is 50 versus 135 for the standard method, translating to a reduction in required sample size of approximately 63%. CONCLUSIONS: We encourage trialists to use the software demonstrated to implement the augmented methodology in future studies to improve efficiency.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectSoftware
dc.subjectEpidemiology and public health
dc.subjectComposite responder endpoint
dc.subjectAugmented binary method
dc.subjectSystemic lupus erythematosus
dc.subjectShiny
dc.titleIncreasing power in the analysis of responder endpoints in rheumatology: a software tutorial.
dc.typeArticle
dc.date.updated2021-12-24T14:36:51Z
prism.issueIdentifier1
prism.publicationNameBMC Rheumatol
prism.volume5
dc.identifier.doi10.17863/CAM.79248
dcterms.dateAccepted2021-08-16
rioxxterms.versionofrecord10.1186/s41927-021-00224-0
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidMcMenamin, Martina [0000-0001-7784-2271]
dc.identifier.eissn2520-1026
pubs.funder-project-idMedical Research Council (MC_UU_00002/6)
cam.issuedOnline2021-12-07


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