NMA-package: The 'NMA' package.
In NMA: Network Meta-Analysis Based on Multivariate Meta-Analysis and Meta-Regression Models
NMA-package R Documentation
The 'NMA' package.
Description
Network meta-analysis tools based on contrast-based approach using the multivariate meta-analysis and meta-regression models (Noma et al., 2025). Comprehensive analysis tools for network meta-analysis and meta-regression (e.g., synthesis analysis, ranking analysis, and creating league table) are available through simple commands. For inconsistency assessment, the local and global inconsistency tests based on the Higgins' design-by-treatment interaction model are available. In addition, the side-splitting methods and Jackson's random inconsistency model can be applied. Standard graphical tools for network meta-analysis, including network plots, ranked forest plots, and transitivity analyses, are also provided. For the synthesis analyses, the Noma-Hamura's improved REML (restricted maximum likelihood)-based methods (Noma et al. (2023ab)) are adopted as the default methods.
References
Higgins, J. P., Jackson, D., Barrett, J. K., Lu, G., Ades, A. E., and White, I. R. (2012). Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Research Synthesis Methods 3, 98-110.
Nikolakopoulou, A., White, I. R., and Salanti, G. (2021). Network meta-analysis. In: Schmid CH, Stijnen T, White IR, eds. Handbook of Meta-Analysis. CRC Press; pp. 187-217.
Noma, H. (2024a). Sidesplitting using network meta-regression. Japanese Journal of Biometrics 44, 107-118.
Noma, H. (2024b). Within-study covariance estimators for network meta-analysis with contrast-based approach. Japanese Journal of Biometrics 44, 119-126.
Noma, H., Hamura, Y., Gosho, M., and Furukawa, T. A. (2023a). Kenward-Roger-type corrections for inference methods of network meta-analysis and meta-regression. Research Synthesis Methods 14, 731-741.
Noma, H., Hamura, Y., Sugasawa, S., and Furukawa, T. A. (2023b). Improved methods to construct prediction intervals for network meta-analysis. Research Synthesis Methods 14, 794-806.
Noma, H. and Maruo, K. (2025). Network meta-analysis combining survival and count outcome data: A simple frequentist approach. medRxiv, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1101/2025.01.23.25321051")}.
Noma, H., Maruo, K., Tanaka, S. and Furukawa, T. A. (2025). NMA: Network meta-analysis based on multivariate meta-analysis and meta-regression models in R. medRxiv, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1101/2025.09.15.25335823")}.
Noma, H., Tanaka, S., Matsui, S., Cipriani, A., and Furukawa, T. A. (2017). Quantifying indirect evidence in network meta-analysis. Statistics in Medicine 36, 917-927.
Salanti, G. (2012). Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods 3, 80-97.
White, I. R., Barrett, J. K., Jackson, D., and Higgins, J. P. (2012). Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Research Synthesis Methods 3, 111-125.
NMA documentation built on Nov. 5, 2025, 7:15 p.m.
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| NMA-package | R Documentation |
The 'NMA' package.
Description
Network meta-analysis tools based on contrast-based approach using the multivariate meta-analysis and meta-regression models (Noma et al., 2025). Comprehensive analysis tools for network meta-analysis and meta-regression (e.g., synthesis analysis, ranking analysis, and creating league table) are available through simple commands. For inconsistency assessment, the local and global inconsistency tests based on the Higgins' design-by-treatment interaction model are available. In addition, the side-splitting methods and Jackson's random inconsistency model can be applied. Standard graphical tools for network meta-analysis, including network plots, ranked forest plots, and transitivity analyses, are also provided. For the synthesis analyses, the Noma-Hamura's improved REML (restricted maximum likelihood)-based methods (Noma et al. (2023ab)) are adopted as the default methods.
References
Higgins, J. P., Jackson, D., Barrett, J. K., Lu, G., Ades, A. E., and White, I. R. (2012). Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Research Synthesis Methods 3, 98-110.
Nikolakopoulou, A., White, I. R., and Salanti, G. (2021). Network meta-analysis. In: Schmid CH, Stijnen T, White IR, eds. Handbook of Meta-Analysis. CRC Press; pp. 187-217.
Noma, H. (2024a). Sidesplitting using network meta-regression. Japanese Journal of Biometrics 44, 107-118.
Noma, H. (2024b). Within-study covariance estimators for network meta-analysis with contrast-based approach. Japanese Journal of Biometrics 44, 119-126.
Noma, H., Hamura, Y., Gosho, M., and Furukawa, T. A. (2023a). Kenward-Roger-type corrections for inference methods of network meta-analysis and meta-regression. Research Synthesis Methods 14, 731-741.
Noma, H., Hamura, Y., Sugasawa, S., and Furukawa, T. A. (2023b). Improved methods to construct prediction intervals for network meta-analysis. Research Synthesis Methods 14, 794-806.
Noma, H. and Maruo, K. (2025). Network meta-analysis combining survival and count outcome data: A simple frequentist approach. medRxiv, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1101/2025.01.23.25321051")}.
Noma, H., Maruo, K., Tanaka, S. and Furukawa, T. A. (2025). NMA: Network meta-analysis based on multivariate meta-analysis and meta-regression models in R. medRxiv, \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1101/2025.09.15.25335823")}.
Noma, H., Tanaka, S., Matsui, S., Cipriani, A., and Furukawa, T. A. (2017). Quantifying indirect evidence in network meta-analysis. Statistics in Medicine 36, 917-927.
Salanti, G. (2012). Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods 3, 80-97.
White, I. R., Barrett, J. K., Jackson, D., and Higgins, J. P. (2012). Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Research Synthesis Methods 3, 111-125.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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