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dc.contributor.authorHerbert, Robert D.
dc.contributor.authorKasza, Jessica
dc.contributor.authorBø, Kari
dc.date.accessioned2018-09-25T08:12:46Z
dc.date.available2018-09-25T08:12:46Z
dc.date.created2018-06-01T10:50:37Z
dc.date.issued2018
dc.identifier.citationBMC Medical Research Methodology. 2018, 18, 48.nb_NO
dc.identifier.issn1471-2288
dc.identifier.urihttp://hdl.handle.net/11250/2564240
dc.descriptionThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.nb_NO
dc.description.abstractRandomised trials with long-term follow-up can provide estimates of the long-term effects of health interventions. However, analysis of long-term outcomes in randomised trials may be complicated by problems with the administration of treatment such as non-adherence, treatment switching and co-intervention, and problems obtaining outcome measurements arising from loss to follow-up and death of participants. Methods for dealing with these issues that involve conditioning on post-randomisation variables are unsatisfactory because they may involve the comparison of non-exchangeable groups and generate estimates that do not have a valid causal interpretation. We describe approaches to analysis that potentially provide estimates of causal effects when such issues arise. Brief descriptions are provided of the use of instrumental variable and propensity score methods in trials with imperfect adherence, marginal structural models and g-estimation in trials with treatment switching, mixed longitudinal models and multiple imputation in trials with loss to follow-up, and a sensitivity analysis that can be used when trial follow-up is truncated by death or other events. Clinical trialists might consider these methods both at the design and analysis stages of randomised trials with long-term follow-up.nb_NO
dc.language.isoengnb_NO
dc.subjectClinical trialsnb_NO
dc.subjectRandomized controlled trialsnb_NO
dc.subjectLong-term follow-upnb_NO
dc.subjectNon-compliancenb_NO
dc.subjectTreatment switchingnb_NO
dc.subjectCo-interventionnb_NO
dc.subjectLoss to follow-upnb_NO
dc.titleAnalysis of randomised trials with long-term follow-upnb_NO
dc.title.alternativeAnalysis of randomised trials with long-term follow-upnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© 2018 Herbert et al.nb_NO
dc.source.pagenumber9nb_NO
dc.source.volume18nb_NO
dc.source.journalBMC Medical Research Methodologynb_NO
dc.source.issue48nb_NO
dc.identifier.doi10.1186/s12874-018-0499-5
dc.identifier.cristin1588277
dc.description.localcodeSeksjon for idrettsmedisinske fag / Department of Sports Medicinenb_NO
cristin.unitcode150,34,0,0
cristin.unitnameSeksjon for idrettsmedisinske fag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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