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dc.contributor.authorCrowley, Een_US
dc.contributor.authorTreweek, Sen_US
dc.contributor.authorBanister, Ken_US
dc.contributor.authorBreeman, Sen_US
dc.contributor.authorConstable, Len_US
dc.contributor.authorCotton, Sen_US
dc.contributor.authorDuncan, Aen_US
dc.contributor.authorEl Feky, Aen_US
dc.contributor.authorGardner, Hen_US
dc.contributor.authorGoodman, Ken_US
dc.contributor.authorLanz, Den_US
dc.contributor.authorMcDonald, Aen_US
dc.contributor.authorOgburn, Een_US
dc.contributor.authorStarr, Ken_US
dc.contributor.authorStevens, Nen_US
dc.contributor.authorValente, Men_US
dc.contributor.authorFernie, Gen_US
dc.date.accessioned2020-06-29T17:04:37Z
dc.date.available2020-05-06en_US
dc.date.issued2020-06-16en_US
dc.identifier.other535
dc.identifier.other535en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/65301
dc.description.abstractBACKGROUND: Data collection consumes a large proportion of clinical trial resources. Each data item requires time and effort for collection, processing and quality control procedures. In general, more data equals a heavier burden for trial staff and participants. It is also likely to increase costs. Knowing the types of data being collected, and in what proportion, will be helpful to ensure that limited trial resources and participant goodwill are used wisely. AIM: The aim of this study is to categorise the types of data collected across a broad range of trials and assess what proportion of collected data each category represents. METHODS: We developed a standard operating procedure to categorise data into primary outcome, secondary outcome and 15 other categories. We categorised all variables collected on trial data collection forms from 18, mainly publicly funded, randomised superiority trials, including trials of an investigational medicinal product and complex interventions. Categorisation was done independently in pairs: one person having in-depth knowledge of the trial, the other independent of the trial. Disagreement was resolved through reference to the trial protocol and discussion, with the project team being consulted if necessary. KEY RESULTS: Primary outcome data accounted for 5.0% (median)/11.2% (mean) of all data items collected. Secondary outcomes accounted for 39.9% (median)/42.5% (mean) of all data items. Non-outcome data such as participant identifiers and demographic data represented 32.4% (median)/36.5% (mean) of all data items collected. CONCLUSION: A small proportion of the data collected in our sample of 18 trials was related to the primary outcome. Secondary outcomes accounted for eight times the volume of data as the primary outcome. A substantial amount of data collection is not related to trial outcomes. Trialists should work to make sure that the data they collect are only those essential to support the health and treatment decisions of those whom the trial is designed to inform.en_US
dc.format.extent535 - ?en_US
dc.languageengen_US
dc.relation.ispartofTrialsen_US
dc.rightsCreative Commons Attribution 4.0 International License
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleUsing systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project.en_US
dc.typeArticle
dc.rights.holder© The Author(s). 2020
dc.identifier.doi10.1186/s13063-020-04388-xen_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/32546192en_US
pubs.issue1en_US
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
pubs.volume21en_US
dcterms.dateAccepted2020-05-06en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.funder.project483cf8e1-88a1-4b8b-aecb-8402672d45f8en_US


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Creative Commons Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International License