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

TL;DR

This paper introduces influence diagnostics methods for evaluating inconsistency in network meta-analysis, providing quantitative tools to identify and prioritize designs that impact overall results, addressing limitations of traditional statistical tests.

Contribution

The paper proposes four new influence diagnostics methods and a summary measure for assessing the impact of individual designs in network meta-analysis, offering an alternative to existing test-based approaches.

Findings

New methods accurately identified sources of inconsistency.

Influence diagnostics provide quantitative assessment of design impact.

Application to antihypertensive drugs demonstrated effectiveness.

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.