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The second key issue is selective outcome reporting. This refers to either: non-reporting of pre-specified trial outcomes; or reporting of non-pre-specified trial outcomes. This study by An-Wen Chan and colleagues found that 50% of efficacy and 65% of harm outcomes per trial were incompletely reported, and that statistically significant outcomes had higher odds of being fully reported compared with non-significant outcomes.
However, the good news is that the US Department of Health and Human Services has recently decided to take a stronger stance on this and get tough on transparency. As of January 2017, those researchers not complying with mandatory results reporting may be subject to substantial fines, and may also have all future NIH grant funding withheld.
SOAR - Duke Clinical Research Institute YODA – Yale ACCESS – CVS Project Data Sphere- Project Data Sphere, LLC (PDS), an independent, not-for-profit initiative of the CEO Roundtable on Cancer's Life Sciences Consortium (LSC), operates the Project Data Sphere platform, a free digital library-laboratory that provides one place where the research community can broadly share, integrate and analyze historical, patient-level data from academic and industry phase III cancer clinical trials.
Despite US legislation mandating results reporting for specific types of trials, compliance has been poor. This study by Prayle and colleagues found that only 22% of studies subject to mandatory results reporting actually complied.
Since 2005, the International Committee of Medical Journal Editors (known as ICMJE) has required authors to prospectively register their trials in order to be considered for publication in a member journal. More recently, in January this year, the ICMJE issued a proposal to require that authors include a plan for data sharing as a component of clinical trial registration. Furthermore, they also propose that as a condition of consideration for publication in a member journal, authors must share with others the de-identified individual participant data underlying the results presented in the article no later than 6 months after publication.
Clinical trials data sharing
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
About half of clinical trials conducted in Australia do not test drugs or devices and do not have industry involvement
50% of research is not published
But similar across countries, size, phase, …
National Database for
Clinical Trials Related
to Mental Illness
UK results on data sharing attitudes
• In 2011 survey, 85% of researchers said they
thought their data would be of interest to
• 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
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
*Simes RJ. Publication bias: the case for an international registry of clinical trials. J Clin Oncol. 1986
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
Available at www.who.int/trialsearch
Better enforcement measures and /
or incentives to share data??
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
• data errors, inconsistencies with published
results - often unresolvable, MIA data manager
• different definitions of key outcomes
FAIR Guiding Principles for Scientific
Data Management and Stewardship
Why curate my clinical trial data?
• costly, time consuming, ongoing resourcing
• no academic or commercial recognition
(in reality system penalises collaboration and
• what incentives can be implemented?
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