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External control arms are becoming increasingly important in drug development, but creating valid comparisons requires more than matching patient populations.
In this episode, I speak with Ben Ackerman, Director of Real-World Biostatistics at GSK, about one of the most overlooked challenges in external control arm studies: endpoint bias. We discuss why differences in how outcomes are measured can influence study results, what researchers should consider when designing studies, and how the field is evolving to address these challenges.
If you work with real-world evidence, causal inference, or innovative clinical trial designs, this episode offers valuable insights into improving the credibility and transparency of external control arm analyses.
**Why You Should Listen **
- Learn why endpoint alignment matters as much as population matching.
- Understand how measurement differences can create bias in external control arm studies.
- Discover practical methods to quantify and mitigate endpoint bias.
- Hear how regulators are increasingly evaluating endpoint comparability.
- Gain insights into better study design and pre-specification strategies for real-world evidence research.
**Episode Highlights **
- 00:01:31 – Introducing Ben Ackerman and external control arms
- 00:04:41 – Why endpoint bias deserves more attention
- 00:08:38 – Understanding the challenges of comparing different data sources
- 00:12:30 – Practical considerations for study design
- 00:16:32 – The role of transparency and pre-specification
- 00:20:30 – Regulatory perspectives and future expectations
- 00:26:07 – Where the field is heading next
**About Ben Ackerman ** Ben Ackerman is Director of Real-World Biostatistics at GSK and a PhD biostatistician specializing in causal inference, real-world evidence methods, and the integration of randomized trial data with observational data sources. His research focuses on improving evidence generation through innovative statistical methods that bridge clinical trials and real-world healthcare data.
