Abstract
Traditionally, clinical trials influence science through publications of their results. Increasingly, however, summary-level and individual participant-level results data are being shared with the scientific community and are influencing science through data reuse. Journal and funder mandates are compelling the sharing of summary-level as well as individual participant-level data (IPD) by industry and academic trialists. Patients, too, are becoming more vocal in demanding that their data contributions to clinical trials be re-used to accelerate findings.
The move toward clinical trial data sharing is part of a wider movement toward open science in general. Four principles underlie scientific data sharing: Findability, Accessibility, Interoperability, and Reusability (FAIR). To handle the global volume of clinical trials, automated implementation of these principles is needed to complement more manual methods. Close to 100 clinical trial data sharing platforms currently exist worldwide, each meeting the FAIR data sharing principles to varying degrees of automation.
This chapter reviews the history, motivations, and current landscape of clinical trial data sharing and reuse. A culture of data sharing is now the norm in the pharmaceutical industry and is starting to take hold in academia, where new mechanisms for crediting and rewarding data sharing are needed. The benefits of data sharing go beyond new publishable findings to include improvements in future study designs informed by analyses of prior IPD. Clinical trial data sharing honors participant contributions to research, enhances public trust in clinical trials, and promises to accelerate scientific findings by maximizing the value of clinical trials data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Blasimme A, Fadda M, Schneider M, Vayena E (2018) Data sharing for precision medicine: policy lessons and future directions. Health Aff (Millwood) 37:702–709. https://doi.org/10.1377/hlthaff.2017.1558
Bot BM, Suver C, Neto EC et al (2016) The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data 3:160011. https://doi.org/10.1038/sdata.2016.11
CDISC (2019a) Study data tabulation model (SDTM). In: Study Data Tabul. Model SDTM. https://www.cdisc.org/standards/foundational/sdtm. Accessed 28 Jun 2018
CDISC (2019b) Analysis data model (ADaM). In: Anal. Data Model ADaM. https://www.cdisc.org/standards/foundational/adam. Accessed 13 Apr 2019
CDISC (2019c) CDISC therapeutic areas. In: Publ. User Guid. https://www.cdisc.org/standards/therapeutic-areas. Accessed 13 Apr 2019
Christakis DA, Zimmerman FJ (2013) Rethinking reanalysis. JAMA 310:2499–2500. https://doi.org/10.1001/jama.2013.281337
ClinicalStudyDataRequest.com Metrics. In: Metrics. https://clinicalstudydatarequest.com/Metrics.aspx. Accessed 13 Apr 2019
Coady SA, Mensah GA, Wagner EL et al (2017) Use of the National Heart, Lung, and Blood Institute data repository. N Engl J Med 376:1849–1858. https://doi.org/10.1056/NEJMsa1603542
Debray TPA, Moons KGM, van Valkenhoef G et al (2015) Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Res Synth Methods 6:293–309. https://doi.org/10.1002/jrsm.1160
Dickersin K (1990) The existence of publication bias and risk factors for its occurrence. JAMA 263:1385–1389
DICOM (2019) DICOM standard. In: DICOM Stand. https://www.dicomstandard.org/. Accessed 13 Apr 2019
Dodd S, Clarke M, Becker L et al (2018) A taxonomy has been developed for outcomes in medical research to help improve knowledge discovery. J Clin Epidemiol 96:84–92. https://doi.org/10.1016/j.jclinepi.2017.12.020
FDA (2019) Real world evidence. In: Real World Evid. https://www.fda.gov/ScienceResearch/SpecialTopics/RealWorldEvidence/default.htm. Accessed 13 Apr 2019
Guinney J, Saez-Rodriguez J (2018) Alternative models for sharing confidential biomedical data. Nat Biotechnol 36:391–392. https://doi.org/10.1038/nbt.4128
ICHOM – International Consortium for Health Outcomes Measurement (2019) ICHOM Standard Sets. In: ICHOM – Int. Consort. Health Outcomes Meas. https://www.ichom.org/standard-sets/. Accessed 9 Dec 2018
Melander H, Ahlqvist-Rastad J, Meijer G, Beermann B (2003) Evidence b(i)ased medicine – selective reporting from studies sponsored by pharmaceutical industry: review of studies in new drug applications. BMJ 326:1171–1173. https://doi.org/10.1136/bmj.326.7400.1171
Mello MM, Lieou V, Goodman SN (2018) Clinical trial participants’ views of the risks and benefits of data sharing. N Engl J Med 378:2202–2211. https://doi.org/10.1056/NEJMsa1713258
National Institutes of Health (2018) Proposed provisions for a draft NIH data management and sharing policy. National Institutes of Health, Bethesda
National Library of Medicine (2019) NIH common data elements (CDE) repository. In: NIH Common Data Elem. CDE Repos. https://cde.nlm.nih.gov/. Accessed 9 Apr 2019
Nature RD at S (2018) Accelerating data sharing. In: Res. Data Springer Nat. https://researchdata.springernature.com/users/8075-grace-baynes/posts/40275-how-can-we-accelerate-data-sharing. Accessed 6 Nov 2018
PhRMA, EFPIA (2013) Principles for responsible clinical trial data sharing. Principles for responsible clinical trial data sharing. http://phrma-docs.phrma.org/sites/default/files/pdf/PhRMAPrinciplesForResponsibleClinicalTrialDataSharing.pdf. Accessed 4 May 2020
Piwowar HA, Vision TJ (2013) Data reuse and the open data citation advantage. PeerJ 1. https://doi.org/10.7717/peerj.175
Piwowar HA, Day RS, Fridsma DB (2007) Sharing detailed research data is associated with increased citation rate. PLoS One 2:e308. https://doi.org/10.1371/journal.pone.0000308
Ross JS, Waldstreicher J, Bamford S et al (2018) Overview and experience of the YODA project with clinical trial data sharing after 5 years. Sci Data 5:180268. https://doi.org/10.1038/sdata.2018.268
Sim I, Chan A-W, Gülmezoglu AM et al (2006) Clinical trial registration: transparency is the watchword. Lancet 367:1631–1633. https://doi.org/10.1016/S0140-6736(06)68708-4
Sim I, Tu SW, Carini S et al (2014) The ontology of clinical research (OCRe): an informatics foundation for the science of clinical research. J Biomed Inform 52:78–91. https://doi.org/10.1016/j.jbi.2013.11.002
Sim I, Wood J, Baskaran A et al (under review) Vivli: a practical implementation of FAIR clinical trial data sharing. Trials
Simes RJ (1986) Publication bias: the case for an international registry of clinical trials. J Clin Oncol Off J Am Soc Clin Oncol 4:1529–1541. https://doi.org/10.1200/JCO.1986.4.10.1529
Taichman DB, Sahni P, Pinborg A et al (2017) Data sharing statements for clinical trials – a requirement of the International Committee of Medical Journal Editors. N Engl J Med 376:2277–2279. https://doi.org/10.1056/NEJMe1705439
The National Academies Press (2015) Sharing clinical trial data: maximizing benefits, minimizing risk. The National Academies Press, Washington, DC
The National Academies Press (2017) Real-world evidence generation and evaluation of therapeutics: proceedings of a workshop. In: Real-World Evid. Gener. Eval. Ther. Proc. Workshop. http://www.nationalacademies.org/hmd/Reports/2017/real-world-evidence-generation-and-evaluation-of-therapeutics-proceedings.aspx. Accessed 13 Apr 2019
Thomas J, Noel-Storr A, Marshall I et al (2017) Living systematic reviews: 2. Combining human and machine effort. J Clin Epidemiol 91:31–37. https://doi.org/10.1016/j.jclinepi.2017.08.011
Tierney JF, Pignon J-P, Gueffyier F et al (2015) How individual participant data meta-analyses have influenced trial design, conduct, and analysis. J Clin Epidemiol 68:1325–1335. https://doi.org/10.1016/j.jclinepi.2015.05.024
Turner EH, Matthews AM, Linardatos E et al (2008) Selective publication of antidepressant trials and its influence on apparent efficacy. N Engl J Med 358:252–260. https://doi.org/10.1056/NEJMsa065779
U.S. National Library of Medicine NIH Data Sharing Repositories. https://www.nlm.nih.gov/NIHbmic/nih_data_sharing_repositories.html. Accessed 8 Apr 2019
Vicente-Saez R, Martinez-Fuentes C (2018) Open Science now: a systematic literature review for an integrated definition. J Bus Res 88:428–436. https://doi.org/10.1016/j.jbusres.2017.12.043
Wilkinson MD, Dumontier M, Aalbersberg IjJ, et al (2016) The FAIR guiding principles for scientific data management and stewardship. In: Sci Data https://www.nature.com/articles/sdata201618. Accessed 28 Jun 2018
Zarin DA, Tse T, Williams RJ, Rajakannan T (2017) The status of trial registration eleven years after the ICMJE policy. N Engl J Med 376:383–391. https://doi.org/10.1056/NEJMsr1601330
(2007) Food and Drug Administration Amendments Act
(2014) European Medicines Agency policy on publication of clinical data for medicinal products for human use
(2017) Re-identification of “anonymized” data. In: Georget Law Technol Rev. https://georgetownlawtechreview.org/re-identification-of-anonymized-data/GLTR-04-2017/. Accessed 13 Apr 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this entry
Cite this entry
Sim, I. (2022). Data Sharing and Reuse. In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52636-2_190
Download citation
DOI: https://doi.org/10.1007/978-3-319-52636-2_190
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52635-5
Online ISBN: 978-3-319-52636-2
eBook Packages: Mathematics and StatisticsReference Module Computer Science and Engineering