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")
::install_github("dmphillippo/multinma") devtools
```

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 0.7.1, 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.