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 is 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 helps 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.
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
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