Network meta-analysis and diffusion.

IF 8 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Research Synthesis Methods Pub Date : 2026-10-15 DOI:10.1017/rsm.2026.10123

Gerta Rücker, Annabel Louisa Davies, Guido Schwarzer

引用次数: 0

Abstract

We show that the covariance matrix of the treatment effect estimates in a network meta-analysis can be obtained without matrix inversion using a geometric series of diffusion matrices. This property extends to the hat matrix and provides a connection between parameter estimation in regression analysis and random walks on the network graph. We also provide a number of visualization tools implemented in R.

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网络荟萃分析和扩散。

我们证明了网络元分析中治疗效果估计的协方差矩阵可以使用扩散矩阵的几何级数而不需要矩阵反演。这一性质扩展到帽矩阵，并提供了回归分析中的参数估计和网络图上的随机行走之间的联系。我们还提供了一些用R实现的可视化工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES

CiteScore

16.90

自引率

3.10%

发文量

期刊介绍： Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.