Exposure-response (ER) analysis is a critical component of clinical drug development for decisions such as dose selection. The new R package, BayesERtools (https://genentech.github.io/BayesERtools/), is designed to make Bayesian ER analysis more accessible. It provides a user-friendly interface for common tasks such as model development, simulation, and plotting, streamlining the entire workflow. The package currently supports linear and Emax models for continuous and binary endpoints. To further support users, we have also developed BayesERbook (https://genentech.github.io/BayesERbook/), a comprehensive online book that documents the workflow with typical examples. This open-source project, based on the Stan ecosystem, aims to expand the use of Bayesian methods in pharma, providing a powerful tool for quantitative decision-making in drug development.
Kenta Yoshida
Genentech
BayesERtools: R package for exposure-response analysis with Bayesian approaches
Exposure-response (ER) analysis is a critical component of clinical drug development for decisions such as dose selection. The new R package, BayesERtools (https//genentech.github.io/BayesERtools/), is designed to make Bayesian ER analysis more accessible. It provides a user-friendly interface for common tasks such as model development, simulation, and plotting, streamlining the entire workflow. The package currently supports linear and Emax models for continuous and binary endpoints. To further support users, we have also developed BayesERbook (https//genentech.github.io/BayesERbook/), a comprehensive online book that documents the workflow with typical examples. This open-source project, based on the Stan ecosystem, aims to expand the use of Bayesian methods in pharma, providing a powerful tool for quantitative decision-making in drug development.