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

Le résumé fourni par la source

Abstract 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. Highlights What is already known? 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. 13 , has been developed. What is new? We developed NMA , a comprehensive R package for network meta-analysis based on multivariate meta-analysis and meta-regression models with frequentist approach. The NMA package provides a broad range of functions for evidence synthesis, heterogeneity and inconsistency assessment, comparative effectiveness analysis, and graphical visualization. Key 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. 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 RSM readers 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. 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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.