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")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.
Carpenter, Bob, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. “Stan: A Probabilistic Programming Language.” Journal of Statistical Software 76 (1). https://doi.org/10.18637/jss.v076.i01.
Phillippo, David Mark. 2019. “Calibration of Treatment Effects in Network Meta-Analysis Using Individual Patient Data.” PhD thesis, University of Bristol.
Phillippo, David M., Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Alexander Schacht, Daniel Saure, Zbigniew Kadziola, and Nicky 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.
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