Path-Based Approach for Detecting and Assessing Inconsistency in Network Meta-Analysis: A Novel Method

TL;DR

This paper introduces a novel path-based method for detecting and visualizing inconsistency in network meta-analysis, improving upon existing techniques by considering all evidence sources collectively.

Contribution

The paper presents a new path-based approach that captures all evidence sources simultaneously and provides visualization tools, enhancing inconsistency detection in NMA.

Findings

Effectively detects inconsistencies in real-world NMA data.

Visualizes multiple path inconsistencies with Netpath plot.

Outperforms traditional methods in comprehensive inconsistency assessment.

Abstract

Network Meta-Analysis (NMA) plays a pivotal role in synthesizing evidence from various sources and comparing multiple interventions. At its core, NMA relies on integrating both direct evidence from head-to-head comparisons and indirect evidence from different paths that link treatments through common comparators. A key aspect is evaluating consistency between direct and indirect sources. Existing methods to detect inconsistency, although widely used, have limitations. For example, they do not account for differences within indirect sources or cannot estimate inconsistency when direct evidence is absent. In this paper, we introduce a path-based approach that explores all sources of evidence without separating direct and indirect. We introduce a measure based on the square of differences to quantitatively capture inconsistency, and propose a Netpath plot to visualize inconsistencies between various paths. We provide an implementation of our path-based method within the netmeta R package. Via application to fictional and real-world examples, we show that our method is able to detect and visualize inconsistency between multiple paths of evidence that would otherwise be masked by considering all indirect sources together. The path-based approach therefore provides a more comprehensive evaluation of inconsistency within a network of treatments.