NMA: Network meta-analysis based on multivariate meta-analysis and meta-regression models in R

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 effect measure 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. HighlightsWhat is already known? O_LISeveral 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. 13, has been developed. C_LI What is new? O_LIWe developed NMA, a comprehensive R package for network meta-analysis based on multivariate meta-analysis and meta-regression models with frequentist approach. C_LIO_LIThe NMA package provides a broad range of functions for evidence synthesis, heterogeneity and inconsistency assessment, comparative effectiveness analysis, and graphical visualization. C_LIO_LIKey analytical tools--such as Higgins global inconsistency test 12, network meta-regression, and advanced inferential and prediction methods to address invalidity issues of the ordinary approaches 17,21 --are fully implemented. C_LIO_LIGeneric 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. C_LI Potential impact for RSM readers O_LIThe 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. C_LIO_LIBy 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. C_LI