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://www.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. (2024). “Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis”. arXiv:2401.12640.
The multinma package can be cited as follows:
Phillippo, D. M. (2024). multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data. R package version 0.7.2.9000, 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. (2024). “Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis”. arXiv:2401.12640.
Carpenter, B., A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker, J. Guo, P. Li, and A. Riddell. 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, M. Belger, A. Brnabic, A. Schacht, D. Saure, Z. Kadziola, and N. J. Welton. 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. https://doi.org/10.1111/rssa.12579.