Drawing conclusions from network meta-analysis
Anna Miroshnychenko, McMaster University
Gordon Guyatt, McMaster University
Farid Foroutan, McMaster University
Background: Although the ability of ranking treatments is usually listed as one of the advantages of network meta-analysis (NMA), rankings fail to consider other pieces of relevant information and may lead to misleading conclusions. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group has developed two frameworks for drawing conclusions from NMA: a minimally contextualized framework and a partially contextualized framework. The frameworks establish that drawing appropriate conclusions from NMA requires explicit consideration of estimates of effect for each pairwise comparison, their certainty of evidence, and the rankings.
Objectives: For individuals considering conducting an NMA, to gain familiarity on how to draw conclusions from network meta-analysis appropriately considering the estimates of effect, the quality of the evidence, and the rankings.
Description: This workshop will focus on the minimally contextualized framework for a single outcome. The framework allows for the classification of interventions in groups, from the most effective to the least effective (or from the most harmful to the least harmful, depending on the outcome). The workshop will begin with an interactive lecture providing details of the framework and then review a step-by-step template for applying it. Workshop participants will then break into groups of five or six to work through an example of a network meta-analysis, guided by facilitators when needed. Finally, the large group will discuss the results and other details of the use of the framework.