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https://github.com/dmphillippo/multinma
Network meta-analysis of individual and aggregate data in Stan
https://github.com/dmphillippo/multinma
r r-package r-stats statistics
Last synced: 24 days ago
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Network meta-analysis of individual and aggregate data in Stan
- Host: GitHub
- URL: https://github.com/dmphillippo/multinma
- Owner: dmphillippo
- Created: 2019-08-02T14:07:58.000Z (over 5 years ago)
- Default Branch: develop
- Last Pushed: 2024-09-24T09:15:34.000Z (about 1 month ago)
- Last Synced: 2024-09-30T02:43:21.486Z (about 1 month ago)
- Topics: r, r-package, r-stats, statistics
- Language: HTML
- Homepage: https://dmphillippo.github.io/multinma
- Size: 802 MB
- Stars: 34
- Watchers: 4
- Forks: 16
- Open Issues: 16
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
---
output: github_document
bibliography: inst/REFERENCES.bib
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# multinma: Network Meta-Analysis of individual and aggregate data in Stan 
[](https://CRAN.R-project.org/package=multinma)
[](https://dmphillippo.r-universe.dev)
[](https://github.com/dmphillippo/multinma/actions)
[](https://doi.org/10.5281/zenodo.3904454)
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 [@methods_paper;@Phillippo_thesis].
Models are estimated in a Bayesian framework using Stan [@Carpenter2017].
## Installation
You can install the released version of `multinma` from [CRAN](https://CRAN.R-project.org/package=multinma) with:
``` r
install.packages("multinma")
```
The development version can be installed from [R-universe](https://dmphillippo.r-universe.dev) with:
```r
install.packages("multinma", repos = c("https://dmphillippo.r-universe.dev", getOption("repos")))
```
or from source on [GitHub](https://github.com/dmphillippo/multinma) with:
``` r
# 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](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started).
## 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 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](https://doi.org/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](https://arxiv.org/abs/2401.12640).
## Citing multinma
The `multinma` package can be cited as follows:
> Phillippo, D. M. (`r format(Sys.Date(), "%Y")`). _multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data_. R package version `r getNamespaceVersion("multinma")`, doi: [10.5281/zenodo.3904454](https://doi.org/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](https://doi.org/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](https://arxiv.org/abs/2401.12640).
## References