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.
Using a mix of simulated and real world case studies, this webinar covers:
- 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
Why It Matters
Traditional meta-analyses often fall short in handling variability across trials, limiting their usefulness in decision-making. MBMA overcomes this by offering a more robust, model-based framework—enabling better synthesis of heterogeneous data, covariate exploration, and trial outcome simulation. This makes it particularly valuable for:
- Comparing treatments when head-to-head data is limited
- Optimizing dose selection and trial design
- Reducing development risk and cost through simulation-based insights
