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 <paynestatistics at gmail.com> BugReports: https://github.com/rich-payne/brisk/issues License: MIT + file LICENSE URL: https://rich-payne.github.io/brisk/ NeedsCompilation: no Materials: README CRAN checks: brisk results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=brisk to link to this page.
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