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Biostatistics, Epidemiology, and Research Design (BERD)

Access to biostatistical, epidemiologic, and study design expertise is critical to the success of clinical and translational research (CTR) in a team science setting. Spectrum’s Biostatistics, Epidemiology, and Research Design (BERD) resources provide CTR investigators with support for study design development, data curation and management, and analytic strategies.

Overview

As new data science practice methods emerge (e.g., electronic medical records, biologic, imaging, physiologic, and administrative), novel clinical trial designs and analytic approaches are needed to answer questions across the translational spectrum. The ability to take full advantage of this data explosion is necessary for an investigator of the 21st century, and requires a broader approach than we have historically taken with a focus on biostatistics and epidemiology.

BERD leverages the infrastructure of the Quantitative Sciences Unit (QSU) in the Department of Medicine led by Dr. Manisha Desai, with additional high-level expertise from faculty in the Department of Biomedical Data Science led by Dr. Sylvia Plevritis. The QSU is a team science-based collaboratory of more than 25 data scientists at the faculty, Ph.D., and Masters level. The Department of Biomedical Data Science houses 19 primary faculty with diverse statistical and data science expertise.

BERD Resources

BERD engages clinical and basic science investigators on:

  • Study design
  • State-of-the-art secure database design
  • Modern data analysis planning
  • Interpretation of findings
  • Development of new methods, tools, software, and applications
  • Education and training in research methods
     

BERD provides the infrastructure for such resources through several critical means – 1) Data Studio; 2) Clinical Office Hours; and 3) Data Science Navigation.

Contact

Clinical and basic science investigators at Stanford can engage BERD members on their research by initiating contact with BERD here:

Additional Data Science Resources

BERD members collaborate closely with and rely heavily on other data science teams on campus. BERD will facilitate relationships with these teams on behalf of our peer investigators.

  • Research Informatics Center: The Research Informatics Center offers a consultation service to Stanford University and Stanford Medicine researchers on topics related to clinical data access for research purposes.
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  • Research IT Technology & Digital Solutions: The Research IT team assist with the following; Data management strategies, Databases, Software development, Common Data Models, Data types and sources, Data collection and storage, Analytical environments, Regulatory, Security and Privacy, Stanford processes, Institutional, multi-party requirements and the Hospital ecosystem.
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  • Technology & Digital Solutions: Provides Stanford Medicine community with the most innovative technology services as efficiently as possible. Led by Eric Yablonka, CIO, and Michael Halaas, deputy CIO, the newly unified organization brings together the best of the School of Medicine and Stanford Health Care IT to enable new opportunities for groundbreaking work and compassionate care.
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  • Center for Population Health Sciences: The Center’s mission is to improve the health of populations by bringing together diverse disciplines and data to understand and address social, environmental, behavioral, and biological factors on both a domestic and global scale.
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  • Stanford’s Department of Biomedical Data Science: The Department of Biomedical Data Science (DBDS) is an academic research community, comprised of faculty, students, and staff, whose mission is to advance precision health by leveraging large, complex, multi-scale real-world data through the development and implementation of novel analytical tools and methods.
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  • Stanford’s Quantitative Sciences Unit: The Quantitative Sciences Unit (QSU) is a unit of statistical scientists in the Department of Medicine who engage in interdisciplinary research.  Members of the QSU are available to collaborate on a broad data science scope including study design, data management and data analysis for research and clinical studies. 
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