A0645
Title: Nonparametric Bayesian approach to treatment ranking in network meta-analysis
Authors: Andres Felipe Barrientos - Florida State University (United States)
Garritt Page - BYU (United States)
Lifeng Lin - University of Arizona (United States) [presenting]
Abstract: Network meta-analysis is a powerful tool to synthesize evidence from independent studies and compare multiple treatments simultaneously. A critical task of performing a network meta-analysis is to offer ranks of all available treatment options for a specific disease outcome. Frequently, the estimated treatment rankings are accompanied by a large amount of uncertainty, suffer from multiplicity issues, and rarely permit possible ties of treatments with similar performance. These issues make interpreting rankings problematic as they are often treated as absolute metrics. To address these shortcomings, a ranking strategy is formulated that adapts to scenarios with high-order uncertainty by producing more conservative results. This improves the interpretability while simultaneously accounting for multiple comparisons. To admit ties between treatment effects in cases where differences between treatment effects are negligible, a Bayesian nonparametric approach is also developed for network meta-analysis. The approach capitalizes on the induced clustering mechanism of Bayesian nonparametric methods, producing a positive probability that two treatment effects are equal. The utility of the procedure is demonstrated through numerical experiments and a network meta-analysis designed to study antidepressant treatments.
