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Network meta-analysis is a powerful tool to synthesize evidence fromindependent studies and compare multiple treatments simultaneously. A criticaltask of performing a network meta-analysis is to offer ranks of all availabletreatment options for a specific disease outcome. Frequently, the estimatedtreatment rankings are accompanied by a large amount of uncertainty, sufferfrom multiplicity issues, and rarely permit ties. These issues makeinterpreting rankings problematic as they are often treated as absolutemetrics. To address these shortcomings, we formulate a ranking strategy thatadapts to scenarios with high order uncertainty by producing more conservativeresults. This improves the interpretability while simultaneously accounting formultiple comparisons. To admit ties between treatment effects, we also developa Bayesian Nonparametric approach for network meta-analysis. The approachcapitalizes on the induced clustering mechanism of Bayesian Nonparametricmethods producing a positive probability that two treatment effects are equal.We demonstrate the utility of the procedure through numerical experiments and anetwork meta-analysis designed to study antidepressant treatments.

Nonparametric Bayesian Approach to Treatment Ranking in Network Meta-Analysis with Application to Comparisons of Antidepressants