Clinical trial data reuse - overcoming complexities in trial design and data sharing.


Journal

Trials
ISSN: 1745-6215
Titre abrégé: Trials
Pays: England
ID NLM: 101263253

Informations de publication

Date de publication:
19 Aug 2019
Historique:
received: 22 01 2019
accepted: 31 07 2019
entrez: 21 8 2019
pubmed: 21 8 2019
medline: 11 2 2020
Statut: epublish

Résumé

There are many acknowledged benefits for the reuse of clinical trial data; from independent verification of published results to the evaluation of new hypotheses. However, the reuse of shared clinical trial data is not without obstacles. Here we present some of the issues and lessons learned from our own experiences in accessing and analyzing trial data; specifically, where we aim to combine and pool data from multiple different trials. In addition to issues around missing annotation and incomplete datasets, we identify trial-design complexity as a potential hurdle that may complicate downstream analyses. We address potential solutions and emphasize the need for benefits of transparent sharing and analysis of participant-level clinical trial data with appropriate risk mitigation, a matter important to efficient clinical research.

Identifiants

pubmed: 31426840
doi: 10.1186/s13063-019-3627-6
pii: 10.1186/s13063-019-3627-6
pmc: PMC6701093
doi:

Types de publication

Letter

Langues

eng

Sous-ensembles de citation

IM

Pagination

513

Subventions

Organisme : Medical Research Council
ID : MR/L011808/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N00583X/1
Pays : United Kingdom

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Auteurs

Toby Wilkinson (T)

Centre for Health Informatics, Division of Informatics, Imaging, and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

Siddharth Sinha (S)

Centre for Health Informatics, Division of Informatics, Imaging, and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

Niels Peek (N)

Centre for Health Informatics, Division of Informatics, Imaging, and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

Nophar Geifman (N)

Centre for Health Informatics, Division of Informatics, Imaging, and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK. nophar.geifman@manchester.ac.uk.

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Classifications MeSH