Applied Modeling in Drug Development Using brms

Interpreting clinical data with applied statistical models is crucial to inform drug development. However, since clinical data comes in diverse forms and presents various statistical challenges, building models requires a lot of flexibility regarding the statistical model being applied. The course introduces the R package brms, which addresses the needs of applied modeling. brms is short for 'Bayesian regression models using Stan' and uses as backend the state-of-the art MCMC sampler Stan. This user-friendly tool has a simple R syntax and can fit a wide range of models. The software features will be illustrated through eight case studies on various drug development problems, including a longitudinal continuous endpoint problem (MMRM), time-to-event analysis, and non-linear modeling for dose finding. Familiarity with R is recommended, but no prior knowledge of Bayesian statistics is required for the half-day course. The course is recommended for all statisticians interested in applied modeling. In addition, a website is available for self-study with further in-depth material (https://opensource.nibr.com/bamdd/).