# Tipping point analysis in network meta-analysis

## TL;DR

This paper introduces a new sensitivity analysis method for network meta-analysis to assess how uncertain correlations between treatments affect conclusions.

## Contribution

The novel tipping point analysis in a Bayesian framework for arm-based network meta-analysis is introduced.

## Key findings

- Tipping points were identified in 11.6% of treatment pairs for interval conclusion change.
- Magnitude changes occurred in 25.9% of treatment pairs with a 15% threshold.
- The analysis is recommended for networks with sparse data or wide credible intervals.

## Abstract

Network meta-analysis (NMA) enables simultaneous assessment of multiple treatments by combining both direct and indirect evidence. While NMAs are increasingly important in healthcare decision-making, challenges remain due to limited direct comparisons between treatments. This data sparsity complicates the accurate estimation of correlations among treatments in arm-based NMA (AB-NMA). To address these challenges, we introduce a novel sensitivity analysis tool tailored for AB-NMA. This study pioneers a tipping point analysis within a Bayesian framework, specifically targeting correlation parameters to assess their influence on the robustness of conclusions about relative treatment effects. The analysis explores changes in the conclusion based on whether the 95% credible interval includes the null value (referred to as the interval conclusion) and the magnitude of point estimates. Applying this approach to multiple NMA datasets, including 112 treatment pairs, we identified tipping points in 13 pairs (11.6%) for interval conclusion change and in 29 pairs (25.9%) for magnitude change with a threshold at 15%. These findings underscore potential commonality in tipping points and emphasize the importance of our proposed analysis, especially in networks with sparse direct comparisons or wide credible intervals for correlation estimates. A case study provides a visual illustration and interpretation of the tipping point analysis. We recommend integrating this tipping point analysis as a standard practice in AB-NMA.