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beastt

https://github.com/gsk-biostatistics/beastt

Science Score: 26.0%

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Last synced: 9 months ago · JSON representation

Repository

Basic Info
Statistics
  • Stars: 4
  • Watchers: 2
  • Forks: 1
  • Open Issues: 7
  • Releases: 3
Created over 2 years ago · Last pushed 10 months ago
Metadata Files
Readme Changelog License

README.Rmd 

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# beastt

## Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials (beastt)



[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) [![CRAN status](https://www.r-pkg.org/badges/version/beastt)](https://CRAN.R-project.org/package=beastt) [![R-CMD-check](https://github.com/GSK-Biostatistics/beastt/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/GSK-Biostatistics/beastt/actions/workflows/R-CMD-check.yaml)
[![codecov](https://codecov.io/gh/GSK-Biostatistics/beastt/graph/badge.svg?token=UVH8SF4OXT)](https://app.codecov.io/gh/GSK-Biostatistics/beastt)




## Overview

Welcome to the {[beastt](https://gsk-biostatistics.github.io/beastt/)} package! This R package is designed to assist users in performing Bayesian dynamic borrowing with covariate adjustment via inverse probability weighted robust mixture priors for simulations and data analyses in clinical trials. For the sake of this package, we use the term IPW BDB to refer to this inverse probability weighted robust mixture methodology.

## What is IPW BDB?
Inverse Probability Weighted Bayesian Dynamic Borrowing (IPW BDB) is a statistical approach designed to enhance the estimation of marginal (i.e., population-averaged or unconditional) treatment effects in clinical trials. This method employs inverse probability weighted robust mixture priors to adjust for covariate differences between a new internal study and external (i.e., historical) control data.

## Why use IPW BDB?
By using propensity score-based inverse probability weighting, IPW BDB effectively balances prognostic variables between trial participants and historical controls, improving inference accuracy and reducing biases due to differences in covariate distributions. This technique increases the statistical power and reduces potential biases in estimating average treatment effects, which are critical for health policy decisions and drug approval processes.

IPW BDB has two mechanisms by which it can account for drift from different sources:
1. The use of a robust mixture prior alleviates prior-data conflict by dynamically down weighting external data when there is a significant level of drift between studies.
2. Inverse probability weighting can account for explainable causes of drift by balancing covariate distributions between external and internal control participants.

*Augmenting the standard robust mixture prior (RMP) approach to incorporate IPWs does not add substantial computational burden associated with other Bayesian approaches*; e.g., in cases where conjugate priors exist for the standard RMP approach, they will still exist for the IPW BDB approach.

## When can IPW BDB be used?
IPW BDB should be considered in clinical trial settings where individual level external control data is available and you want to integrate this data with your current trial. This method is particularly useful when there are differences in distributions of key prognostic factors between the current study population and the external controls, which could otherwise introduce bias. It is especially relevant in oncology and rare disease trials, where using external data can help overcome challenges such as slow patient enrollment due to reluctance to join control groups. IPW BDB is well-suited for contexts where Bayesian dynamic borrowing is already applicable but could benefit from additional adjustments for confounding.

## Installation

You can install the development version of {beastt} from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("GSK-Biostatistics/beastt")
```

## Usage

At the moment {beastt} covers borrowing from external control data for normal, binary, and time to event endpoints. For more information, see the vignettes.

## Contributing

Feel free to contribute to the {beastt} package by reporting issues or submitting pull requests on the GitHub repository.

## License

This package is released under GLP-3.

Owner

  • Name: GSK Biostatistics
  • Login: GSK-Biostatistics
  • Kind: organization

GitHub Events

Total
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  • Pull request event: 101
Last Year
  • Create event: 62
  • Commit comment event: 2
  • Release event: 2
  • Issues event: 50
  • Watch event: 2
  • Delete event: 55
  • Issue comment event: 39
  • Push event: 295
  • Pull request review comment event: 35
  • Pull request review event: 60
  • Pull request event: 101

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 54
  • Total pull requests: 116
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 5 days
  • Total issue authors: 7
  • Total pull request authors: 5
  • Average comments per issue: 0.33
  • Average comments per pull request: 0.5
  • Merged pull requests: 103
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 33
  • Pull requests: 91
  • Average time to close issues: 20 days
  • Average time to close pull requests: 3 days
  • Issue authors: 6
  • Pull request authors: 3
  • Average comments per issue: 0.27
  • Average comments per pull request: 0.56
  • Merged pull requests: 81
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • statasaurus (34)
  • bcarancibia (8)
  • abiterry (7)
  • nwbean (3)
  • erp31 (2)
  • eddelbuettel (1)
  • dragosmg (1)
Pull Request Authors
  • abiterry (68)
  • statasaurus (57)
  • nwbean (17)
  • ShmoopyE (6)
  • bcarancibia (5)
Top Labels
Issue Labels
enhancement (5) debating (2) discussion (2) documentation (1) question (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 522 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
  • Total maintainers: 1
cran.r-project.org: beastt

Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 522 Last month
Rankings
Dependent packages count: 28.7%
Dependent repos count: 35.4%
Average: 50.1%
Downloads: 86.3%
Maintainers (1)
christina.e.fillmore@gsk.com
Last synced: 10 months ago

Dependencies

.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/pr-commands.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/pr-fetch v2 composite
  • r-lib/actions/pr-push v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/test-coverage.yaml actions
  • actions/checkout v3 composite
  • actions/upload-artifact v3 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
DESCRIPTION cran
  • cli * imports
  • dplyr * imports
  • generics * imports
  • ggplot2 * imports
  • purrr * imports
  • rlang * imports
  • stringr * imports
  • testthat >= 3.0.0 suggests