Development of a Privacy and Security Policy Framework for a Multistate Comparative Effectiveness Research Network

Medical Care ◽  
2013 ◽  
Vol 51 ◽  
pp. S66-S72 ◽  
Author(s):  
Katherine K. Kim ◽  
Deven McGraw ◽  
Laura Mamo ◽  
Lucila Ohno-Machado
Keyword(s):  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Security Policy ◽  
Research Network ◽  
Policy Framework ◽  
Effectiveness Research ◽  
Privacy And Security

scholarly journals International Comparison of Approaches to Common Data Models for Comparative Effectiveness Research

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Adrian Levy ◽  
Robert Platt ◽  
Soko Setoguchi ◽  
Jeffrey Brown ◽  
Michael Paterson
Keyword(s):  
Comparative Effectiveness Research ◽  
Data Model ◽  
Comparative Effectiveness ◽  
Distributed Networks ◽  
Network Governance ◽  
Data Models ◽  
Research Network ◽  
Common Data Model ◽  
Effectiveness Research ◽  
The Us

Over the past decade, characterizing the safety and effectiveness of drugs has advanced through distributed networks of data repositories where investigators implement the same procedures to address the same topic using a common data model. Distributed networks for pharmacoepidemiology have now been established in the United States (US), Globally/Europe Canada, and Asian countries. Sentinel in the US was developed in response to legislation and is funded by the US Food and Drug Administration to address their safety queries. The Observational Medical Outcomes Partnership (OMOP) is an international collaborative with a growing European data network that developed a common data model through a public-private partnership. The Canadian Network of Observational Drug Effect Studies (CNODES) receives funding and study queries from Health Canada and dissemination is directly back to the regulator as well as through the peer-reviewed literature. The Asian Pharmacoepidemiology Network (AsPEN) is an investigator-initiated multi-national research network formed to support the safety and effectiveness assessment of medications and other therapeutics and to facilitate the prompt identification and validation of emerging safety issues among the countries in Asia and Pacific regions. While these networks have implemented two different common data models (CNODES with Sentinel, ASPEN with OMOP), each network differs from the others in the aims, stage and implementation, operational approach, data quality assurance mechanisms, funding, and dissemination. The objectives of this session are to compare and contrast the role and goals, design principles, implementation approaches, and analytic conventions and procedures between common data models implemented by SENTINEL, OMOP, CNODES, ands AsPEN. Divided into seven 15-minute segments the session begins with an overview of distributed networks of common data models for pharmacoepidemiology. In four slides, each presenter then characterizes their network by describing the following: number of data holders, lives covered, and records, data holdings, data access model, network governance. process for transforming a repository’s data into the common data model target audience(s), process of identifying queries and knowledge dissemination plan two key challenges faced by the network and the lessons learned In identifying similarities and meaningful differences between the networks, in the next segment the discussant will articulate the relative strengths of the different approaches taken. This will lead into the last segment in which the floor will be opened for questions and comments from the audience. The session would be of benefit to researchers seeking to better understand or join an existing distributed network as well as researchers interested in broadening their understanding of global comparative effectiveness research.

A Practice-Based Research Network Focused on Comparative Effectiveness Research in Type 2 Diabetes Management

2013 ◽  
Vol 125 (3) ◽  
pp. 172-179 ◽  
Author(s):  
John E. Anderson ◽  
Andrew S. Rhinehart ◽  
Timothy S. Reid ◽  
Robert M. Cuddihy ◽  
Aleksandra Vlajnic ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Diabetes Management ◽  
Research Network ◽  
Effectiveness Research ◽  
Practice Based Research

scholarly journals Publication of Tumor Marker Research Results: The Necessity for Complete and Transparent Reporting

2012 ◽  
Vol 30 (34) ◽  
pp. 4223-4232 ◽  
Author(s):  
Lisa M. McShane ◽  
Daniel F. Hayes
Keyword(s):  
Tumor Marker ◽  
Tumor Markers ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Clinical Utility ◽  
The Body ◽  
Selective Reporting ◽  
Effectiveness Research ◽  
Study Quality ◽  
Transparent Reporting

Clinical management decisions for patients with cancer are increasingly being guided by prognostic and predictive markers. Use of these markers should be based on a sufficiently comprehensive body of unbiased evidence to establish that benefits to patients outweigh harms and to justify expenditure of health care dollars. Careful assessments of the clinical utility of markers by using comparative effectiveness research methods are urgently needed to more rigorously summarize and evaluate the evidence, but multiple factors have made such assessments difficult. The literature on tumor markers is plagued by nonpublication bias, selective reporting, and incomplete reporting. Several measures to address these problems are discussed, including development of a tumor marker study registry, greater attention to assay analytic performance and specimen quality, use of more rigorous study designs and analysis plans to establish clinical utility, and adherence to higher standards for reporting tumor marker studies. More complete and transparent reporting by adhering to criteria such as BRISQ [Biospecimen Reporting for Improved Study Quality] criteria for reporting details about specimens and REMARK [Reporting Recommendations for Tumor Marker Prognostic Studies] criteria for reporting a multitude of aspects relating to study design, analysis, and results, is essential for reliable assessment of study quality, detection of potential biases, and proper interpretation of study findings. Adopting these measures will improve the quality of the body of evidence available for comparative effectiveness research and enhance the ability to establish the clinical utility of prognostic and predictive tumor markers.

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