Aetion
April 1, 2021
COVID-19 Evidence Accelerator: A parallel analysis to describe the use of hydroxychloroquine with or without azithromycin among hospitalized COVID-19 patients
The COVID-19 Evidence Accelerator’s Therapeutics Parallel Analysis Workgroup published its first manuscript in PLOS One, which shares how several organizations conducted analyses in their own real-world data (RWD) sets to determine whether hydroxychloroquine and azithromycin, alone or in combination, could be used to treat COVID-19.
Organized by the Reagan-Udall Foundation for the U.S. Food and Drug Administration (FDA) and Friends of Cancer Research (Friends), the COVID-19 Evidence Accelerator joins leaders in the health care data and analytics space to explore how RWD can inform key COVID-19 research questions.
During this parallel analysis, researchers from Friends, the Reagan-Udall Foundation for the FDA, Aetion, HealthVerity, and other organizations conducted parallel analyses in seven different real-world databases to describe use of hydroxychloroquine and/or azithromycin among hospitalized COVID-19 patients.
Ultimately, this first parallel analysis project helped lay the methodological and logistical groundwork for future collaborations using different RWD sources to generate rapid insights on COVID-19, helping advance the understanding of the disease and its interventions.
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Organized by the Reagan-Udall Foundation for the U.S. Food and Drug Administration (FDA) and Friends of Cancer Research (Friends), the COVID-19 Evidence Accelerator joins leaders in the health care data and analytics space to explore how RWD can inform key COVID-19 research questions.
During this parallel analysis, researchers from Friends, the Reagan-Udall Foundation for the FDA, Aetion, HealthVerity, and other organizations conducted parallel analyses in seven different real-world databases to describe use of hydroxychloroquine and/or azithromycin among hospitalized COVID-19 patients.
Ultimately, this first parallel analysis project helped lay the methodological and logistical groundwork for future collaborations using different RWD sources to generate rapid insights on COVID-19, helping advance the understanding of the disease and its interventions.