NMA-package {NMA}

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., 2023c). Standard analysis tools for network meta-analysis and meta-regression (e.g., synthesis analysis, ranking analysis, and creating league table) are available by simple commands. For inconsistency analyses, the local and global inconsistency tests based on the Higgins' design-by-treatment interaction model can be applied. Also, the side-splitting and the Jackson's random inconsistency model are available. Standard graphical tools for network meta-analysis (e.g., network plot, ranked forest plot, and transitivity analysis) can also be utilized. 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. (2023a). Sidesplitting using network meta-regression. Jxiv, 493. doi:10.51094/jxiv.493.

Noma, H. (2023b). Within-study covariance estimators for network meta-analysis with contrast-based approach. Jxiv, 490. doi:10.51094/jxiv.490.

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., Maruo, K., Tanaka, S. and Furukawa, T. A. (2023c). NMA: R package for network meta-analysis based on the multivariate meta-analysis and meta-regression models. Forthcoming.

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.