Properly understanding the probability of successful drug development requires utilization of all available information. Critical drug development decisions cannot be made with internal data alone. Model-based meta-analysis (MBMA) extracts important insights contained within both proprietary data and publicly available clinical trial results, thus enabling critical R&D, financial (e.g., in/out-licensing), and commercial decision-making with the highest confidence.
In this webinar, Drs. Jaap Mandema and Matt Zierhut will explain how MBMA can improve drug development by:
- Rigorously establishing safety and efficacy targets needed for differentiation
- Providing a quantitative framework to properly understand the probability of achieving those differentiation targets (probability of technical success)
- Rapidly integrating newly available data (both proprietary and published) into this quantitative framework, ensuring decisions are based on all available information
- Using simulations to design cost-effective drug development programs and improve the probability of correct decisions (e.g., Go/No-go)
- Simplifying knowledge transfer from detailed trial networks
