MBNMAtime

R package for time-course Model-Based Network Meta-Analysis

https://github.com/hugaped/mbnmatime

Science Score: 23.0%

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Repository

R package for time-course Model-Based Network Meta-Analysis

Basic Info
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  • Stars: 7
  • Watchers: 0
  • Forks: 1
  • Open Issues: 3
  • Releases: 1
Created almost 7 years ago · Last pushed over 1 year 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%"
)
```


[![CRAN status](https://www.r-pkg.org/badges/version/MBNMAtime)](https://CRAN.R-project.org/package=MBNMAtime)
[![R-CMD-check](https://github.com/hugaped/MBNMAtime/workflows/R-CMD-check/badge.svg)](https://github.com/hugaped/MBNMAtime/actions)


# MBNMAtime

The goal of `MBNMAtime` is to provide a collection of useful commands that allow users to run time-course Model-Based Network Meta-Analysis (MBNMA). This allows meta-analysis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons.

Including all available follow-up measurements within a study makes use of all the available evidence in a way that maintains connectivity between treatments, and it does so in a way that explains time-course, thus explaining heterogeneity and inconsistency that may be present in a 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 Lu and Ades [-@lu2004] and are run in JAGS [@jags]. For full details of time-course MBNMA methodology see Pedder et al. [-@pedder2019].


## Installation

Currently the package is available on [CRAN](https://CRAN.R-project.org) and can can be installed using:

``` r
install.packages("MBNMAtime")
```

The development version can be installed directly from GitHub using the `devtools` R package:

``` r
# First install devtools
install.packages("devtools")

# Then install MBNMAtime directly from GitHub
devtools::install_github("hugaped/MBNMAtime")
```

Note that JAGS (Just Another Gibbs Sampler) must be installed for `MBNMAtime` to function correctly. The latest version can be downloaded from: https://sourceforge.net/projects/mcmc-jags/files/latest/download

Note that if using Windows OS with R version >= 4.2, the JAGS version must be >= 4.3.1.

## Workflow

Functions within `MBNMAtime` follow a clear pattern of use:

1. Load your data into the correct format using `mb.network()`
2. Specify a suitable time-course function and analyse your data using `mb.run()`
3. Test for consistency using functions like `mb.nodesplit()`
4. Examine model results using forest plots and treatment rankings
5. Use your model to predict responses or estimate treatment effects at specific time-points using `predict()`

At each of these stages there are a number of informative plots that can be generated to help make sense of your data and the models that you are fitting. Exported functions in the package are connected like so:

*MBNMAtime 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*
![Workflow](man/figures/functionstructure.png)

## References

Owner

  • Name: Hugo Pedder
  • Login: hugaped
  • Kind: user
  • Company: University of Bristol

GitHub Events

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Last synced: over 2 years ago

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Hugo h****r@b****k 617
Hugo h****r@g****m 13
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Last synced: 11 months ago

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Packages

  • Total packages: 1
  • Total downloads:
    • cran 349 last-month
  • Total docker downloads: 42,005
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 7
  • Total maintainers: 1
cran.r-project.org: MBNMAtime

Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 349 Last month
  • Docker Downloads: 42,005
Rankings
Stargazers count: 24.2%
Forks count: 28.8%
Dependent packages count: 29.8%
Average: 32.4%
Dependent repos count: 35.5%
Downloads: 43.9%
Maintainers (1)
Last synced: 11 months ago