forked from dmphillippo/multinma
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
71 lines (50 loc) · 3.5 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
---
output: github_document
bibliography: inst/REFERENCES.bib
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{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 <img src='man/figures/logo.svg' style="float:right" height="139" />
<!-- badges: start -->
[](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)
<!-- badges: end -->
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 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).
## 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).
## References