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

TL;DR

This paper introduces a Bayesian nonparametric method for treatment ranking in network meta-analysis, addressing uncertainty, multiple comparisons, and ties, with applications to antidepressant treatments.

Contribution

It develops a novel Bayesian nonparametric approach that allows treatment effect ties and provides more conservative, interpretable rankings in network meta-analysis.

Findings

Improved treatment ranking interpretability.

Ability to model ties between treatments.

Validated approach through numerical experiments.

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 ties. These issues make interpreting rankings problematic as they are often treated as absolute metrics. To address these shortcomings, we formulate a ranking strategy 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, we also develop a Bayesian Nonparametric approach 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. We demonstrate the utility of the procedure through numerical experiments and a network meta-analysis designed to study antidepressant treatments.