Clinical trial data are a public good, but many stakeholders in addition to the public have interests in those data, observed Jeffrey Nye, Janssen Research & Development, in his introduction to the session on models of data sharing. Participants in a trial have interests in the information a trial generates, as do the researchers conducting a trial. Pharmaceutical companies are another stakeholder, along with researchers from either the private or public sectors doing reanalyses or meta-analyses of study data. Regulators have the objective of safeguarding public health and guiding and advising companies as they develop new products, while citizen scientists may be studying the data to derive information they can apply in their own lives.
Seven speakers at the workshop described different models designed to increase the sharing of clinical research data. All of these models have strengths and limitations. Although the optimal path forward is not yet clear, all of these models offer lessons that can inform future initiatives.
Three key problems interfere with the practice of evidence-based medicine, said Deborah Zarin, director of ClinicalTrials.gov at the National Library of Medicine, National Institutes of Health (NIH). Not all trials are published. Publications do not always include all of the prespecified outcome measures. Unacknowledged changes made to trial protocols can affect the interpretation of findings.
These problems led to the establishment in 2000 of ClinicalTrials.gov, which serves as a registry of clinical trials at the trials’ inception (Zarin et al., 2011). The registry now contains key protocol details of more than 130,000 trials from around the world. In 2008 the registry added a results database, which now contains the summary results of more than 7,000 trials. ClinicalTrials.gov does not accept participant-level data, Zarin emphasized, but it has considerable experience with other kinds of data generated by clinical trials.
Clinical trials data take many forms, from uncoded, participant-level data to analyzed summary data; only the latter are posted at ClinicalTrials.gov. At each step in the process leading from the raw data to the summary data, information is lost (see Figure 4-1). Also, each vertical drop involves subjective judgments that are not transparent, but can