Research Synthesis Methods· 2026Q1
NMA: Network meta-analysis based on multivariate meta-analysis and meta-regression models in R
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- Q1SCImago
- 2026year
Short summary
The NMA R package simplifies complex network meta-analysis by integrating multivariate meta-analysis and meta-regression, offering tools for evidence synthesis, comparative effectiveness, and visualization.
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Key points
- The NMA R package simplifies network meta-analysis for non-statisticians.
- It is based on multivariate meta-analysis and meta-regression frameworks.
- The package offers tools for evidence synthesis, comparative effectiveness, and visualization.
- It supports integration of arm-level data and summary effect estimates.
- A case study on antihypertensive drugs illustrates its application.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Network meta-analysis has become an established methodology within systematic reviews for comparing the effectiveness of multiple treatments and is now a standard approach in comparative effectiveness research. However, the underlying statistical methods are often highly technical for non-statisticians in practice. To complement existing software, we developed NMA , a comprehensive R package organized around the multivariate meta-analysis and meta-regression framework and designed to provide a broad range of analytical and graphical tools through simple commands. The NMA package provides tools for evidence synthesis, network meta-regression, assessment of heterogeneity and inconsistency, comparative effectiveness analyses, and graphical visualization. It also incorporates higher-order inferential and prediction procedures and data-handling functions that facilitate the integration of arm-level data and summary effect estimates. In this article, we provide a gentle introduction to the NMA package and illustrate its application through a case study of a network meta-analysis of antihypertensive drugs.
The authors' abstract, as published at the source. Research Synthesis Methods, 2026 · DOI ↗
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Statistics, Probability and UncertaintyDecision Sciences