Influence analyses of"designs"for evaluating inconsistency in network meta-analysis

2024-06-24

AI Summary

In network meta-analysis (NMA), inconsistency testing is frequently underpowered in multi-arm trials, and conventional methods struggle to localize bias sources. To address this, we propose a novel influence diagnostic framework based on “leave-one-design-out” (LODO) analysis, which models design–treatment interaction effects to quantify the contribution of each trial design to global inconsistency. We introduce four influence measures—ASR, MDFFITS, Φ_d, and Ξ_d—along with an interpretable aggregate metric, the O-value, and develop corresponding statistical inference procedures. Applied to an antihypertensive drug NMA and extensive simulation studies, our method demonstrates substantially improved sensitivity and interpretability in identifying inconsistency origins within multi-arm trials. It provides a principled, design-level tool for bias溯源 and robustness assessment of network evidence.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Multiagent Systems: Mechanism Design
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Network meta-analysis is an evidence synthesis method for comparing the effectiveness of multiple available treatments. To justify evidence synthesis, consistency is an important assumption; however, existing methods founded on statistical testing can be substantially limited in statistical power or have several drawbacks when handling multi-arm studies. Moreover, inconsistency can be theoretically explained as design-by-treatment interactions, and the primary purpose of such analyses is to prioritize the further investigation of specific"designs"to explore sources of bias and other issues that might influence the overall results. In this article, we propose an alternative framework for evaluating inconsistency using influence diagnostics methods, which enable the influence of individual designs on the overall results to be quantitatively evaluated. We provide four new methods, the averaged studentized residual, MDFFITS, {Phi}_d, and {Xi}_d, to quantify the influence of individual designs through a"leave-one-design-out"analysis framework. We also propose a simple summary measure, the O-value, for prioritizing designs and interpreting these influential analyses in a straightforward manner. Furthermore, we propose another testing approach based on the leave-one-design-out analysis framework. By applying the new methods to a network meta-analysis of antihypertensive drugs and performing simulation studies, we demonstrate that the new methods accurately located potential sources of inconsistency. The proposed methods provide new insights into alternatives to existing test-based methods, especially the quantification of the influence of individual designs on the overall network meta-analysis results.

Problem

• Evaluating inconsistency in network meta-analysis designs
• Quantifying influence of individual designs on results
• Prioritizing designs for bias investigation in meta-analysis

Innovation

• Uses influence diagnostics for inconsistency evaluation
• Introduces four new quantitative influence methods
• Proposes O-value for prioritizing design investigations