multinma: Network Meta-Analysis of individual and aggregate data in Stan

CRAN status R build status DOI

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).

Installation

You can install the released version of multinma from CRAN with:

install.packages("multinma")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("dmphillippo/multinma")

Installing from source (either from CRAN or GitHub) requires that the rstan package is installed and configured. See the installation guide here.

Getting started

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 http://nicedsu.org.uk/.

Multilevel network meta-regression is set out in the following 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.

Citing multinma

The multinma package can be cited as follows:

Phillippo, D. M. (2020). multinma: Network Meta-Analysis of Individual and Aggregate Data in Stan. R package version 0.2.0, 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.

References

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.