Monday, Aug 7: 3:05 PM - 3:25 PM
Topic-Contributed Paper Session
Metro Toronto Convention Centre
Quantitative methods for benefit-risk analysis help to condense complex decisions into a univariate metric to describe the overall benefit relative to risk. One such approach is the multi-criteria decision analysis (MCDA) framework. Bayesian benefit-risk analysis incorporates uncertainty through posterior distributions which are inputs to the benefit-risk framework. This talk will focus on the brisk R package (available on CRAN) which provides functions to assist with Bayesian benefit-risk analyses, such as MCDA. The package requires users to input posterior samples, utility functions, weights, and the package outputs posteriors of benefit-risk scores. The software and methods will be illustrated with several examples.