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Clinical trials data sharing

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Presentation by Prof Lisa Askie, ANZCTR, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.

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Clinical trials data sharing

  1. 1. Clinical trials data sharing Prof Lisa Askie Director, Systematic Reviews and Health Technology Assessment NHMRC Clinical Trials Centre University of Sydney, Australia Co-convenor, Cochrane Prospective Meta-analysis Methods Group Member, Cochrane Individual Participant Data Meta-analysis Methods Group Manager, Australian New Zealand Clinical Trials Registry Cochrane Review author Clinical trialist
  2. 2. About half of clinical trials conducted in Australia do not test drugs or devices and do not have industry involvement
  3. 3. 50% of research is not published Lancet 2014;383:257–66 But similar across countries, size, phase, … WASTE
  4. 4. National Database for Clinical Trials Related to Mental Illness (NDCT)
  5. 5. UK results on data sharing attitudes • In 2011 survey, 85% of researchers said they thought their data would be of interest to others • Only 41% said they would be happy to make their data available • Only a third had previously published data Source: DaMaRO Project, University of Oxford http://www.slideshare.net/DigCurv/15-meriel-patrick
  6. 6. Current situation summary • Known about publication bias and selective outcome reporting for over 30 years • Some (mostly US & European) legislation ‘requiring’ data sharing, mostly re drug trials • Compliance with data sharing remains poor • Recent calls for fully open, fully accessible clinical trial datasets to be made available from all trials
  7. 7. *Simes RJ. Publication bias: the case for an international registry of clinical trials. J Clin Oncol. 1986
  8. 8. The University of Sydney Page 12 Prospective trial registration Trial information on the ANZCTR and other Primary registries is imported onto the WHO ICTRP Search Portal Available at www.who.int/trialsearch
  9. 9. Better enforcement measures and / or incentives to share data??
  10. 10. The University of Sydney Page 14
  11. 11. The University of Sydney Page 15
  12. 12. January 2016
  13. 13. Common problems with trial data • inconsistent data coding both within (at different time points) and across trials • lack of a detailed and robust data dictionary and/or meta-data • data errors, inconsistencies with published results - often unresolvable, MIA data manager • different definitions of key outcomes
  14. 14. Data curation not just data sharing
  15. 15. FAIR Guiding Principles for Scientific Data Management and Stewardship Findability Accessibility Interoperability Reusability
  16. 16. Why curate my clinical trial data? • costly, time consuming, ongoing resourcing • no academic or commercial recognition (in reality system penalises collaboration and sharing) • what incentives can be implemented?
  17. 17. April 2017
  18. 18. Take home messages for trialists • data needs to be curated for future use, not just simply dumped or ‘shared’ • assume your trial data will be used by others (with no control by you) in the future for purposes not initially intended • common definitions of key, core outcomes would be very helpful • well documented data dictionaries are crucial • need repositories that can receive, curate, provide access to clinical trial data using FAIR principles