The multinma package implements network meta-analysis, network
meta-regression, and multilevel network meta-regression models which
combine evidence from a network of studies and treatments using either
aggregate data or individual patient data from each study (Phillippo et
al. 2020; Phillippo 2019). Models are estimated in a Bayesian framework
using Stan (Carpenter et al. 2017).
You can install the released version of multinma from
CRAN with:
install.packages("multinma")The development version can be installed from R-universe with:
install.packages("multinma", repos = c("https://dmphillippo.r-universe.dev", getOption("repos")))or from source on GitHub with:
# install.packages("devtools")
devtools::install_github("dmphillippo/multinma")Installing from source requires that the rstan package is installed
and configured. See the installation guide
here.
A good place to start is with the package vignettes which walk through
example analyses, see vignette("vignette_overview") for an overview.
The series of NICE Technical Support Documents on evidence synthesis
gives a detailed introduction to network meta-analysis:
Dias, S. et al. (2011). “NICE DSU Technical Support Documents 1-7: Evidence Synthesis for Decision Making.” National Institute for Health and Care Excellence. Available from https://sheffield.ac.uk/nice-dsu/tsds.
Multilevel network meta-regression is set out in the following methods papers:
Phillippo, D. M. et al. (2020). “Multilevel Network Meta-Regression for population-adjusted treatment comparisons.” Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(3):1189-1210. doi: 10.1111/rssa.12579.
Phillippo, D. M. et al. (2026). “Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis”. Journal of the Royal Statistical Society: Series A (Statistics in Society), 189(3):1856-1875. doi: 10.1093/jrsssa/qnaf169.
The multinma package can be cited as follows:
Phillippo, D. M. (2026). multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data. R package version 0.9.1.9002, doi: 10.5281/zenodo.3904454.
When fitting ML-NMR models, please cite the methods paper:
Phillippo, D. M. et al. (2020). “Multilevel Network Meta-Regression for population-adjusted treatment comparisons.” Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(3):1189-1210. doi: 10.1111/rssa.12579.
For ML-NMR models with time-to-event outcomes, please cite:
Phillippo, D. M. et al. (2026). “Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis”. Journal of the Royal Statistical Society: Series A (Statistics in Society), 189(3):1856-1875. doi: 10.1093/jrsssa/qnaf169.
Carpenter, B., A. Gelman, M. D. Hoffman, et al. 2017. “Stan: A Probabilistic Programming Language.” Journal of Statistical Software 76 (1). https://doi.org/10.18637/jss.v076.i01.
Phillippo, D. M. 2019. “Calibration of Treatment Effects in Network Meta-Analysis Using Individual Patient Data.” PhD thesis, University of Bristol.
Phillippo, D. M., S. Dias, A. E. Ades, et al. 2020. “Multilevel Network Meta-Regression for Population-Adjusted Treatment Comparisons.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 183 (3): 1189–210. https://doi.org/10.1111/rssa.12579.