COVID-19 Evidence Accelerator

Reagan-Udall Foundation for the Food and Drug Administration / Friends of Cancer Research

Go to for the most up-to-date information about both the COVID-19 Therapeutics & Diagnostic Evidence Accelerators.

To sign up for updates on the COVID-19 Evidence Accelerator, click here

COVID-19 Evidence Accelerator

The COVID-19 Evidence Accelerator is an initiative launched by the Reagan-Udall Foundation for the FDA in collaboration with Friends of Cancer Research (Friends) to provide a unique venue for major data organizations, government and academic researchers, and health systems to gather and design quick turnaround queries and share their results.

The Accelerator will bring together the country’s leading experts in health data aggregation and analytics in a unified effort to share insights, compare results, and answer key questions about COVID-19 treatment and response as quickly as possible.

Diagnostics Evidence Accelerator

The COVID-19 Diagnostics Evidence Accelerator is a multi-stakeholder, collaborative project  leveraging real-world data in the diagnostic (e.g., nucleic acid or antigenic) and antibody testing (e.g., serological) space.

The goal is to present and discuss information on recent analyses of real-world data related to test performance, contemporaneous symptoms and presentation, surveillance trends and immunity to peers and experts from FDA, major technology companies, academic research institutions, device manufacturers, payers and health systems. These meetings occur every Thursday from 12-1 pm ET.

Urgency and Action: Since the beginning of the pandemic, data scientists around the country have been engaged in an intense effort to capture real-world data and rapidly deploy data analytics to help answer key questions related to the management of COVID-19 patients.  While over time each of these individual efforts will likely develop into valuable insights, by banding together we can collectively accelerate and maximize the utility of this information in the near term. To do this effectively, a core set of common data elements are being developed that will allow any willing data collection effort to embed these data elements into their on-going work in a uniform way to allow for rapid aggregation and analysis.

Combining efforts will make the findings more robust and accelerate answers.

Participants in the COVID-19 Evidence Accelerator helped develop an initial set of Key Questions and Core Data Elements that could be used in research using various real-world data sets.

Two Interactive Work Streams:

1) Accelerator Parallel Analyses: Developing key research questions that multiple organizations and teams can address simultaneously.

Initial activities of this work stream include (1) rapidly revising a list of core data elements; (2) identifying those critical to answering the primary question; and (3) establishing uniform collection parameters. It will be necessary to work collaboratively to determine how data elements are being extracted and how they are being defined in order to operationalize a platform that can not only answer questions now, but also inform how research activities could be conducted in the future.

Repeating analyses in parallel by collaborators using different analytical techniques and data sources will help strengthen findings and learnings. Furthermore, this effort will help validate the role of real-world evidence as a tool for rapidly learning about patient characteristics, treatment patterns, and outcomes associated with management strategies for COVID-19.

Specific Scenario for Parallel Analysis Project 1:

Among hospitalized patients with COVID-19, describe the following for hydroxychloroquine +/- azithromycin vs control?

  • Characterize COVID-19 patient populations treated with hydroxychloroquine +/- azithromycin vs control
  • Characterize hydroxychloroquine +/- azithromycin treatment (e.g., timing in COVID-19 illness trajectory; monotherapy vs co-prescription; dose)
  • Characterize safety signals with hydroxychloroquine +/- azithromycin vs control, including by subpopulations (e.g., age, diabetes, COPD)
  • Describe comparative effectiveness of hydroxychloroquine +/- azithromycin vs control on key outcomes (see below)
  • Identify potential predictors of treatment safety and effectiveness
  • Validate COVID-19 risk stratification score
  • Click here for Summary of Parallel Analysis Project 1

2) Accelerator Lab Meetings: Share findings from interested data partners on critical questions

Results are generated and analyzed in many different ways and using different methods and data sources. Lab meetings will provide a venue for scientists across the country to discuss data generated from quick turnaround queries and share results with peers and experts from FDA, major data organizations, academic research institutions, professional societies, and health systems to help accelerate, and potentially even confirm, findings from different data sources and leverage existing expertise.


COVID-19 Evidence Accelerator Media Coverage