brisk: Bayesian Benefit Risk Analysis
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 |
| 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:
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