Journal
RESEARCH SYNTHESIS METHODS
Volume 14, Issue 3, Pages 479-488Publisher
WILEY
DOI: 10.1002/jrsm.1627
Keywords
data visualization; dynamic evidence synthesis; medical decision making; online software tool
Ask authors/readers for more resources
Outputs from living evidence syntheses projects have been widely used during the pandemic to provide evidence-based recommendations. However, stakeholders also need to understand the data and perform their own exploratory analyses. To assist them, a metaCOVID application has been created in R to facilitate fast data exploration and tailored sub-analyses.
Outputs from living evidence syntheses projects have been used widely during the pandemic by guideline developers to form evidence-based recommendations. However, the needs of different stakeholders cannot be accommodated by solely providing pre-defined non amendable numerical summaries. Stakeholders also need to understand the data and perform their own exploratory analyses. This requires resources, time, statistical expertise, software knowledge as well as relevant clinical expertise to avoid spurious conclusions. To assist them, we created the metaCOVID application which, based on automation processes, facilitates the fast exploration of the data and the conduct of sub-analyses tailored to end-users needs. metaCOVID has been created in R and is freely available as an R-Shiny application. Based on the COVID-NMA platform () the application conducts living meta-analyses of randomized controlled trials related to COVID-19 treatments and vaccines for several outcomes. Several options are available for subgroup and sensitivity analyses. The results are presented in downloadable forest plots. We illustrate metaCOVID through three examples involving well-known treatments and vaccines for COVID-19. The application is freely available from .
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available