As the volume of Real-World Data (RWD) expands, the pharmaceutical industry faces a critical bottleneck: transforming unstructured data into rigorous, actionable clinical evidence.

This session explores the end-to-end architecture required to achieve this, bridging cutting-edge causal inference methodology with modern data infrastructure and pharmaceutical execution.

Attendees will gain a comprehensive understanding of how advanced statistical frameworks - specifically Targeted Learning and Target Trial Emulation - are moving beyond academic theory to directly impact clinical differentiation and improve the Probability of Success (PoS) in drug development.