dc.contributor.author | Herbert, Robert D. | |
dc.contributor.author | Kasza, Jessica | |
dc.contributor.author | Bø, Kari | |
dc.date.accessioned | 2018-09-25T08:12:46Z | |
dc.date.available | 2018-09-25T08:12:46Z | |
dc.date.created | 2018-06-01T10:50:37Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | BMC Medical Research Methodology. 2018, 18, 48. | nb_NO |
dc.identifier.issn | 1471-2288 | |
dc.identifier.uri | http://hdl.handle.net/11250/2564240 | |
dc.description | This 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.abstract | Randomised 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.iso | eng | nb_NO |
dc.subject | Clinical trials | nb_NO |
dc.subject | Randomized controlled trials | nb_NO |
dc.subject | Long-term follow-up | nb_NO |
dc.subject | Non-compliance | nb_NO |
dc.subject | Treatment switching | nb_NO |
dc.subject | Co-intervention | nb_NO |
dc.subject | Loss to follow-up | nb_NO |
dc.title | Analysis of randomised trials with long-term follow-up | nb_NO |
dc.title.alternative | Analysis of randomised trials with long-term follow-up | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.rights.holder | © 2018 Herbert et al. | nb_NO |
dc.source.pagenumber | 9 | nb_NO |
dc.source.volume | 18 | nb_NO |
dc.source.journal | BMC Medical Research Methodology | nb_NO |
dc.source.issue | 48 | nb_NO |
dc.identifier.doi | 10.1186/s12874-018-0499-5 | |
dc.identifier.cristin | 1588277 | |
dc.description.localcode | Seksjon for idrettsmedisinske fag / Department of Sports Medicine | nb_NO |
cristin.unitcode | 150,34,0,0 | |
cristin.unitname | Seksjon for idrettsmedisinske fag | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |