Michael Cantor, senior director of clinical informatics and innovation at Pfizer Inc., described an ongoing data-sharing project being undertaken by Pfizer as part of its “Data Without Borders” initiative. The project, called ePlacebo, pools data from placebo and control arms across multiple clinical trials in a variety of therapeutic areas. The result is a large comparison group that can be used to evaluate events that might not be seen in a single trial, study placebo effects, and possibly reduce the size of placebo arms needed in future clinical trials. So far, data from about 20,000 patients have been compiled from hundreds of trials, and Pfizer is hoping to expand the utility of this data source by soliciting participation from other organizations.
The goal for ePlacebo is to provide a resource that is inclusive, rests on standards, and spans disease areas. The intent is to set it up as a self-service dataset that could be used for any legitimate research purpose. However, consistent data standards have only been implemented at Pfizer within the past decade and as a result, only relatively recent studies were used for ePlacebo because of the difficulties combining data from trials that did not use standards or implemented them in different ways.
Compton discussed several important governance issues that arose during the CAMD initiative and in other C-Path efforts. First, rules for developing the data standards require collaborative expert input and consensus. Disease definitions need to come from the bottom up, said Compton, from the clinicians who are dealing with patients and diseases. A system cannot be imposed on them from the outside. However, the National Institutes of Health can use its purse strings to enforce clinician-driven, evidence-based guidelines, and perhaps some degree of evidence-based standardization could be regulated. Also, best practices for merging the data call for the use of high-quality data and FDA-accepted standards that work together along the process, from beginning to end. With regard to rules for accessing the data, the broadest possible data use agreements are needed, and access controls need to be appropriate to the use objectives. Finally, qualified drug development tools should be placed in the public domain to maximize their use.