doi:10.1002/pds.1435>. Bayesian benefit-risk analysis incorporates uncertainty through posterior distributions which are inputs to the benefit-risk framework. The brisk package provides functions to assist with Bayesian benefit-risk analyses, such as MCDA. Users input posterior samples, utility functions, weights, and the package outputs quantitative benefit-risk scores. The posterior of the benefit-risk scores for each group can be compared. Some plotting capabilities are also included.">

brisk: Bayesian Benefit Risk Analysis (original) (raw)

Quantitative methods for benefit-risk analysis help to condense complex decisions into a univariate metric describing the overall benefit relative to risk. One approach is to use the multi-criteria decision analysis framework (MCDA), as in Mussen, Salek, and Walker (2007) <doi:10.1002/pds.1435>. Bayesian benefit-risk analysis incorporates uncertainty through posterior distributions which are inputs to the benefit-risk framework. The brisk package provides functions to assist with Bayesian benefit-risk analyses, such as MCDA. Users input posterior samples, utility functions, weights, and the package outputs quantitative benefit-risk scores. The posterior of the benefit-risk scores for each group can be compared. Some plotting capabilities are also included.

Version: 0.1.0
Imports: dplyr (≥ 1.0), ellipsis (≥ 0.3), ggplot2 (≥ 3.3), hitandrun (≥ 0.5), purrr (≥ 0.3), rlang (≥ 1.0), tidyr (≥ 1.1)
Suggests: knitr, fs (≥ 1.5), testthat (≥ 3.0.0), tibble (≥ 3.1), rmarkdown
Published: 2022-08-31
DOI: 10.32614/CRAN.package.brisk
Author: Richard Payne [aut, cre], Sai Dharmarajan [rev], Eli Lilly and Company [cph]
Maintainer: Richard Payne
BugReports: https://github.com/rich-payne/brisk/issues
License: MIT + file
URL: https://rich-payne.github.io/brisk/
NeedsCompilation: no
Materials: README
CRAN checks: brisk results

Documentation:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=briskto link to this page.