NMA: Network meta-analysis based on multivariate meta-analysis and meta-regression models in R
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Network meta-analysis has become an established methodology within systematic reviews for comparing the effectiveness of multiple treatments, and it has been now a standard approach in comparative effectiveness research. However, the underlying statistical methods are often highly technical for non-statisticians in practice, and no freely available software package has been developed that can handle a general framework based on the multivariate meta-analysis and meta-regression models. To address these issues, we developed NMA, a comprehensive and user-friendly R package that covers extensive analysis and graphical tools of network meta-analysis with simple commands. The NMA package provides generic functional tools for evidence synthesis based on the multivariate meta-analysis models, network meta-regression, assessment of heterogeneity and inconsistency, comparative effectiveness analyses, and a range of graphical tools. In addition, NMA includes data-handling functions that facilitate the integration of both arm-level data and summary statistics easily. 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.
Highlights
What is already known?
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Several freely computational packages are available for network meta-analysis, but no general frequentist tool based on the multivariate meta-analysis and meta-regression models, introduced by White et al. 1 , has been developed.
What is new?
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We developed NMA, a comprehensive R package for network meta-analysis based on multivariate meta-analysis and meta-regression models with frequentist approach.
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The NMA package provides a broad range of functions for evidence synthesis, heterogeneity and inconsistency assessment, comparative effectiveness analysis, and graphical visualization.
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Key analytical tools—such as Higgins’ global inconsistency test 2 , network meta-regression, and advanced inferential and prediction methods to address invalidity issues of the ordinary approaches 3,4 —are fully implemented.
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Generic data-handling tools can now integrate arm-level data with summary statistics. This provides greater flexibility in network meta-analysis, making it especially useful for studies of survival outcomes.
Potential impact for readers
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The package facilitates the practical use of network meta-analysis for a broad range of researchers, including non-statisticians, thereby enhancing the accessibility of systematic reviews on important clinical and public health questions.
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By comprehensively covering standard analyses and graphical tools, the NMA package is also valuable for educational purposes, serving as a practical resource for students, researchers, and clinicians to learn the research methods through real-world case studies.
