MI260: Bayesian Model-Based Meta-Analysis to Support Decision Making in Drug Development

MI260 provides an introduction to meta analysis concepts and methods, with a strong focus on model-based meta-analysis of summary data or a combination of summary and individual data from clinical trials to support decision-making in clinical drug development. Upon completion of the course, participants will be able to write a meta-analysis plan, design a model-based meta-analysis of clinical trial data to address strategic decisions in a clinical drug development program, and implement it using a Bayesian approach executed with WinBUGS and R. Participants will also be able to construct a model for the relationship between an efficacy- or safety-related clinical outcome and independent variables such as dose, time and patient characteristics by analysis of summary data from multiple studies, e.g., treatment means and standard deviations, and construct such a model by analysis of a combination of summary and individual data, as well as execute and interpret population simulations to support decision-making in clinical drug development.