RankÔÇÉHeat Plot: An R Shiny tool for presenting multiple network meta-analysis outcomes

Date & Time
Tuesday, September 5, 2023, 2:15 PM - 2:25 PM
Location Name
Session Type
Oral presentation
Evidence synthesis innovations and technology
Oral session
Overviews of reviews, scoping reviews and network meta-analysis
Veroniki AA1, Tricco A1, Watt J1, Straus S1
1Unity Health Toronto; University of Toronto, Canada

Background: The increased number of outcomes and competing interventions assessed in a network meta-analysis (NMA) increases the difficulty of interpreting results and hence challenges their use in everyday clinical practice and policy.
Objectives: To facilitate evidence-based decision-making on a clinical topic with multiple outcomes per intervention through a graphical representation of NMA results, the rank-heat plot. We are currently developing the rankheatplot R package and R Shiny app and will disseminate the first version at the conference.
Methods: The rankheatplot R Shiny app can be used in any type of discipline and disease using an NMA of multiple interventions compared in different studies obtained from any type of review (i.e., systematic review, rapid review, overview of reviews). The rank-heat plot was first developed and published in 2016. It has been cited in more than 120 publications (Google Scholar) and has been used in multiple areas such as geriatrics, pediatrics, neurology, and oncology. It is already part of the viscomp R package, which presents multiple visualization approaches on component NMA. We developed an interactive web application at https://rankheatplot.com/ to produce the rank-heat plot when the study-level data for each outcome are available and without performing the NMA analysis outside the tool. The tool can be used for any type of data.
Results: The rank-heat plot allows the fast identification of the most likely best and worst interventions, with respect to their effectiveness and/or safety, in a given outcome. It can also identify interventions that have not been studied for an included outcome. The rankheatplot app currently performs analyses in a frequentist framework importing intervention outcome results from the netmeta R package. In the next version of the tool, we plan to incorporate the option of conducting the analysis in a Bayesian framework, as well as outputs based on clinically important effects (e.g., minimal clinically important difference).
Conclusions: The rank-heat plot summarizes results on all interventions and all outcomes, providing information regarding the ranking of interventions per outcome. The tool can be used to facilitate interpretation and decision-making based on NMA synthesis. No patients were involved in the development of this tool.