Applied Modelling in Drug Development

Flexible regression modelling in Stan via brms

Preface

This website contains materials that were developed to accompany a series of workshops held in the Analytics department at Novartis over 2022-2024, illustrating the utility of the R package brms for solving drug- development problems. In order to highlight the versatility of that package, we have developed a collection of case studies covering a diverse set of clinical questions that were addressed via Bayesian modelling with the brms package.

Navigating the site

The material is organized as follows:

• 1  Introduction introduces brms and the objectives of these vignettes
• 2  Basic workflow highlights the basic workflow (analysis steps and syntax) for carrying out an analysis with brms
• The next series of sections contain the case studies. You can find a listing with descriptions on this page.
• Sections 15  Exposing stan code and 16  Parallel computation cover more technical topics, such as exposing and injecting custom stan code to brms models, and efficient parallel computation to support sampling in brms.

Updates

This web-site is intended as a live document with updates as appropiate. Key changes to the web-site are tracked here:

• 29th April 2024: Third edition course from Paul Bürkner with the new case studies on probability of success, meta-analytic priors with covariates & time-to-event modelling in Oncology phase I dose-escalation. Pre-read version.
• 19th April 2024: First public version at opensource.nibr.com/bamdd
• 5th January 2024: Release web-site in more modern quarto book based format
• 26th April 2023: Second edition course from Paul Bürkner with the new case studies on MMRM, time-to-event data, surrogate endpoint meta-regression and network meta-analysis
• 27th April 2022: First release with course from Paul Bürkner

License information

• The material on this website is provided under a CC BY 4.0 license
• The source code is provided under a GPL-2 license