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

2025-06-25

AI Summary

Conventional network meta-analysis (NMA) inconsistency assessment methods struggle to accommodate heterogeneity across indirect evidence paths and cannot evaluate inconsistency in the absence of direct comparisons. Method: We propose a unified, evidence-path–based modeling framework that treats all direct and indirect evidence paths equivalently. A squared-difference metric quantifies path-level inconsistency, and the Netpath plot enables intuitive visualization of multi-path discrepancies. Contribution/Results: Our approach transcends the traditional “direct versus indirect” dichotomy and, for the first time, enables inconsistency detection even when no direct comparisons exist. Implemented in the R package *netmeta*, the method demonstrates robust performance in simulation and real-data studies. It identifies multi-path inconsistencies undetected by conventional techniques—such as node-splitting—and substantially enhances the reliability, sensitivity, and interpretability of NMA results.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Detects inconsistency in Network Meta-Analysis evidence paths
• Quantifies inconsistency using squared differences measure
• Visualizes inconsistencies via Netpath plot in R

Innovation

• Path-based method integrates all evidence sources
• Square of differences measures inconsistency quantitatively
• Netpath plot visualizes inconsistencies between paths