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dc.contributor.authorBanerjee, Soumya
dc.contributor.authorSofack, Ghislain N
dc.contributor.authorPapakonstantinou, Thodoris
dc.contributor.authorAvraam, Demetris
dc.contributor.authorBurton, Paul
dc.contributor.authorZöller, Daniela
dc.contributor.authorBishop, Tom RP
dc.date.accessioned2022-06-07T08:12:48Z
dc.date.available2022-06-07T08:12:48Z
dc.date.issued2022-06-03
dc.date.submitted2022-01-05
dc.identifier.issn1756-0500
dc.identifier.others13104-022-06085-1
dc.identifier.other6085
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337783
dc.description.abstractOBJECTIVE: Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but an analytic workflow involving local analysis undertaken at individual studies hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. RESULTS: We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectResearch Note
dc.subjectSurvival analysis
dc.subjectMeta-analysis
dc.subjectFederated analysis
dc.titledsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD.
dc.typeOther
dc.date.updated2022-06-07T08:12:48Z
prism.issueIdentifier1
prism.publicationNameBMC Res Notes
prism.volume15
dc.identifier.doi10.17863/CAM.85192
dcterms.dateAccepted2022-05-24
rioxxterms.versionofrecord10.1186/s13104-022-06085-1
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBanerjee, Soumya [0000-0001-7748-9885]
dc.identifier.eissn1756-0500
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Societal Challenges (824989)
cam.issuedOnline2022-06-03


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