Model-Based Meta-Analysis (MBMA) for Relative Treatment Effects and Absolute Outcomes: A Focus on Concepts and Applications

Model-Based Meta-Analysis (MBMA) has emerged as a powerful tool for integrating data across clinical trials and generating insights that efficiently inform drug development and regulatory decision-making. This webinar will unpack two distinct MBMA methodologies: modeling absolute outcomes and relative treatment effects.

• Choosing the Right Modeling Approach
  When to apply absolute outcome vs. relative effect models based on clinical and development objectives.
• Maximizing Impact with Covariates
  Differentiating between prognostic and predictive covariates and how they influence model applications, plus best practices for covariate exploration.
• Informing Decisions with Simulations
  How MBMA-based simulations can improve trial design, support treatment comparisons, and guide go/no-go decisions.
• Ensuring Model Credibility
  Techniques for evaluating model fit and credibility to ensure reliable, decision-ready results.
• Applying MBMA in the Real World
  Case studies showing how MBMA informs regulatory submissions, product strategy, and development planning.