Sharing Clinical Research Data Workshop Summary (2013) / Chapter Skim
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4 Models of Data Sharing
4 Models of Data Sharing
Pages 27-42
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From page 27... ...
Companies can be fierce competitors, but still cooperate on precompetitive research to meet common needs. If patients provide information for a research project, they should receive information in return that can help them make meaningful health care decisions.
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From page 28... ...
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)
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The data are presented in a tabular format with minimal narrative. They cover participant flows, baseline patient characteristics, outcome measures, and adverse events.
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Although the deposition of clinical trial protocols and summary data into registries is a huge step forward in the direction of transparency, the validity and reproducibility of summary data are called into question by such inconsistencies. "This is a big problem," Zarin asserted.
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Changes in risk behaviors, an increase in screening, and new therapeutics have all contributed to this decline in cancer, "but we are not being as effective as we would like to be." At the same time, the price of cancer treatment has skyrocketed, which is not sustainable in an era of fiscal austerity. We need to find better ways of reducing cancer mortality rates, said Hugh-Jones, and "one of the solutions of many that we need to address is data sharing." Data sharing in the field of oncology could lead to faster and more effective research through improved trial designs and statistical methodology, the development of secondary hypotheses and enhanced understanding of epidemiology, collaborative model development, and smaller trial sizing, said Hugh-Jones.
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"With the sort of environment we have demonstrated here, this is something that can be successful." THE YALE-MEDTRONIC EXPERIENCE One paradigm for facilitating dissemination of industry data and ensuring high-quality independent review of the evidence for efficacy is exemplified by the Yale-Medtronic experience, as described by Richard Kuntz, senior vice president and chief scientific, clinical, and regulatory officer of Medtronic, Inc., where proprietary data were released to an external coordinating organization that contracted other organizations to perform systematic reviews of the study results.
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project, which, according to Kuntz, serves as a model for the dissemination and independent analysis of clinical trial program data. This project is based on the rationale that a substantial number of clinical trials are conducted but never published, and even among published clinical trials, only a limited portion of the collected data is available.
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The YODA project has been designed to promote wider access to clinical trial program data, increase transparency, protect against industry influence, and accelerate the generation of new knowledge. The public has a compelling interest in having the entirety of the data available for independent analysis, but industry has legitimate concerns about the release of data, Kuntz said.
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Nevertheless, the bottom line is that industry has a responsibility to do studies with regulatory agencies to produce results in a faithful and trusted way and to disseminate them under the law. It needs to competently and ethically contract or execute the required clinical studies and perform timely filing of the data and results dossier.
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Its goals are to facilitate the development and validation of new biomarkers; help qualify these biomarkers for specific applications in diagnosing disease, predicting therapeutic response, or improving clinical practice; generate information useful to inform regulatory decision making; and make Consortium project results broadly available to the entire scientific community. John Wagner, vice president for clinical pharmacology at Merck & Co., Inc., described the validation of adiponectin as a biomarker as an example of the work of the Consortium.
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Also, FDA currently has an initiative to require uniform data submissions using standardized data fields, which would result in data that are much more amenable for sharing, Wagner observed. Furthermore, health care reform is also expected to harmonize data practices, in part to reduce costs and improve care.
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From page 38... ...
For unknown reasons, the active-placebo differentiation varies by geographical region, with considerably more differentiation in Eastern Europe than in North America. All of this information, which is useful in its own right, can be used to design more effective and efficient clinical trials with smaller treatment groups and shorter study durations, Rabinowitz stated, which together could significantly reduce costs of drug discovery trials.
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"Just think about that for a second." PATIENTSLIKEME PatientsLikeMe is a health information–sharing website for patients where they can form peer-to-peer relationships, establish profiles, provide and share health data, and make de-identified data available for research. Sally Okun, health data integrity manager at PatientsLikeMe, described some of the lessons learned from the website during its 7 years of operation.
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. He went on to describe the Query Health Initiative, a system for sharing clinical information that has been promulgated by the Office of the National Coordinator for Health Information Technology.
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It has a distributed database with data on more than 125 million people, 3 billion instances of drug Analytic Query Web Portal FIREWALL FIREWALL FIREWALL FIREWALL FIREWALL FIREWALL Approval Site A Site A Site A Notification Query Network Steering Result Committee/IRB Management INTERNET Auditing Query Authentication Site B Site B Site B Result Authorization Query interface Scheduling Query Permissions Aggregation Site C Site C Site C Result Synchronization Authorized Source Research Project User Data Data Data FIGURE 4-2 Distributed networks can facilitate working remotely with research datasets derived from routinely collected electronic health information, often eliminating the need to transfer sensitive data. SOURCE: Platt, 2012.
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From page 42... ...
Toward that end, the week before the workshop, the NIH announced the creation of the Health Care Systems Research Collaborative, which will develop a distributed research network with the capability of communicating with the Mini-Sentinel distributed dataset. Such systems, by sharing information rather than data, could make progress faster than waiting for all the issues surrounding data sharing to be resolved, said Platt.
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