In highly targeted oncology programs where patient populations are small and randomized control trials are difficult to conduct, real world data can play a powerful role in strengthening evidence generation. This case study highlights how a biotechnology company partnered with MMS to evaluate and construct a synthetic control arm using a large real world data registry.

Created for clinical development teams, biometrics leaders, and RWE strategists, this resource explains the process, feasibility considerations, and regulatory context behind using RWD to support evidence in challenging oncology settings. It also demonstrates how rigorous methodology, predefined selection criteria, and advanced analytics can produce meaningful and defensible insights.

By reviewing the complete case study, you will learn how MMS identified the right patient cohort, aligned RWD with trial requirements, and generated a synthetic control arm that reduced burden, saved time, and created a pathway for more efficient clinical execution.

Key Benefits / What You’ll Learn:

• Understand how MMS identified and filtered real world data to match strict eligibility and disease severity criteria.
• Learn how predefined procedures and unbiased patient selection strengthen feasibility and regulatory alignment.
• Explore how synthetic control arms can reduce recruitment challenges and support development in small or rare populations.
• See the real impact including reduced study burden, faster timelines, and validated evidence to support an oncology clinical trial.

Developed by MMS data science experts who apply advanced analytics, proprietary algorithms, and rigorous RWE methodology to support clinical programs worldwide.