Science Score: 59.0%
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○CITATION.cff file
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✓DOI references
Found 2 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (19.9%) to scientific vocabulary
Last synced: 6 months ago
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JSON representation
Repository
R package for dose-response MBNMA
Basic Info
- Host: GitHub
- Owner: hugaped
- Language: HTML
- Default Branch: master
- Homepage: https://hugaped.github.io/MBNMAdose
- Size: 74.5 MB
Statistics
- Stars: 11
- Watchers: 2
- Forks: 0
- Open Issues: 1
- Releases: 2
Created over 6 years ago
· Last pushed 10 months ago
Metadata Files
Readme
Changelog
README.Rmd
---
output: github_document
bibliography: inst/REFERENCES.bib
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-",
out.width = "100%"
)
```
[](https://CRAN.R-project.org/package=MBNMAdose)
[](https://github.com/hugaped/MBNMAdose/actions)
[](https://zenodo.org/badge/latestdoi/195961874)
[](https://github.com/hugaped/MBNMAdose/actions/workflows/R-CMD-check.yaml)
# MBNMAdose 0.4.1
The goal of `MBNMAdose` is to provide a collection of useful commands that allow users to run dose-response Model-Based Network Meta-Analyses (MBNMA). This allows evidence synthesis of studies that compare multiple doses
of different agents in a way that can account for the dose-response relationship.
Whilst making use of all the available evidence in a statistically robust and biologically plausible framework, this also can help connect networks at the agent level that may otherwise be disconnected at the dose/treatment level, and help improve precision of estimates[@pedder2021]. It avoids "lumping" of doses that is often done in standard Network Meta-Analysis (NMA). All models and analyses are implemented in a Bayesian framework, following an extension of the standard NMA methodology presented by [@lu2004] and are run in JAGS (Just Another Gibbs Sampler). For full details of dose-response MBNMA methodology see Mawdsley et al. [-@mawdsley2016]. Throughout this package we refer to a **treatment** as a specific **dose** or a specific **agent**.
A short introductory YouTube video from the ESMAR Conference 2021 can be found [here](https://doi.org/10.6084/m9.figshare.13637936.v1)
## Installation
On CRAN you can easily install the current release version of `MBNMAdose` from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("MBNMAdose")
```
For the development version the package can be installed directly from GitHub using the `devtools` R package:
``` r
# First install devtools
install.packages("devtools")
# Then install MBNMAdose directly from GitHub
devtools::install_github("hugaped/MBNMAdose")
```
## Workflow
Functions within `MBNMAdose` follow a clear pattern of use:
1. Load your data into the correct format using `mbnma.network()` and explore potential relationships
2. Perform a dose-response MBNMA using `mbnma.run()`. Modelling of effect modifying covariates is also possibly using Network Meta-Regression.
3. Test for consistency at the treatment-level using functions like `nma.nodesplit()` and `nma.run()`
4. Examine model outputs, such as relative effects, forest plots and treatment rankings
5. Use your model to predict responses using `predict()`
At each of these stages there are a number of informative plots that can be generated to help understand the data and to make decisions regarding model fitting. Exported functions in the package are connected like so:
*MBNMAdose package structure: Light green nodes represent classes and the generic functions that can be applied to them. Dashed boxes indicate functions that can be applied to objects of specific classes*
## References
Owner
- Name: Hugo Pedder
- Login: hugaped
- Kind: user
- Company: University of Bristol
- Repositories: 2
- Profile: https://github.com/hugaped
GitHub Events
Total
- Push event: 14
- Pull request event: 1
Last Year
- Push event: 14
- Pull request event: 1
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Hugo | h****r@b****k | 594 |
| hugaped | h****r@g****m | 146 |
| Hugo Pedder | h****2@b****k | 77 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 1
- Total pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: 6 months
- Total issue authors: 1
- Total pull request authors: 1
- Average comments per issue: 2.0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- rsbivand (1)
Pull Request Authors
- olivroy (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 310 last-month
- Total docker downloads: 20,392
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 10
- Total maintainers: 1
cran.r-project.org: MBNMAdose
Dose-Response MBNMA Models
- Homepage: https://hugaped.github.io/MBNMAdose/
- Documentation: http://cran.r-project.org/web/packages/MBNMAdose/MBNMAdose.pdf
- License: GPL-3
-
Latest release: 0.5.0
published about 1 year ago
Rankings
Stargazers count: 19.8%
Forks count: 28.8%
Average: 29.5%
Dependent packages count: 29.8%
Downloads: 33.7%
Dependent repos count: 35.5%
Maintainers (1)
Last synced:
6 months ago
Dependencies
DESCRIPTION
cran
- R >= 3.0.2 depends
- R2jags >= 0.5 imports
- Rdpack >= 0.11 imports
- checkmate >= 1.8.5 imports
- dplyr >= 0.7.4 imports
- grDevices * imports
- graphics * imports
- igraph >= 1.1.2 imports
- magrittr >= 1.5 imports
- reshape2 >= 1.4.3 imports
- rgeos >= 0.5 imports
- rjags >= 4 imports
- scales * imports
- stats * imports
- utils * imports
- RColorBrewer >= 1.1 suggests
- coda >= 0.19 suggests
- crayon >= 1.3.4 suggests
- forestplot >= 1.10 suggests
- ggdist >= 2.4.0 suggests
- ggplot2 >= 2.2.1 suggests
- knitr * suggests
- lspline >= 1.0 suggests
- mcmcplots >= 0.4.3 suggests
- overlapping >= 1.5.0 suggests
- rmarkdown * suggests
- testthat >= 1.0.2 suggests