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The FDA Group’s Nick Capman recently sat down with Dr. Manfred Stapff—physician, author, researcher, and founder of Candid Advisory Inc., a consultancy specializing in real-world evidence and clinical development—for a wide-ranging conversation on the evolution and implications of real-world evidence (RWE) in life science research.
Dr. Stapff spent over three decades in medical research and drug development, from hands-on clinical work to leadership positions at Merck, Forest Labs, and TriNetX. His current focus is on helping organizations leverage real-world data (RWD) to generate meaningful insights that inform everything from regulatory decisions to early diagnoses—while also warning against the misuse or misinterpretation of these massive data sets.
Whether you're in clinical operations, regulatory strategy, pharmacovigilance, or just interested in the growing role of AI in healthcare, this episode offers expert insight into how data—and how we handle it—will shape the future of evidence generation in medicine.
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Dr. Stapff’s key takeaways and recommendations
If you’re short on time, here are some standout insights from this episode, especially relevant for regulatory and clinical development professionals.
RWE is not a replacement for clinical trials—it’s a complement. RCTs remain the gold standard for demonstrating safety and efficacy under controlled conditions. But they often exclude elderly patients, at-risk populations, and minority groups, limiting generalizability. RWE fills that gap by showing how a drug or intervention performs in everyday settings. "Both have pros and cons. There’s no winner. They complement each other."
Data becomes evidence through quality and context. Real-world data is just that: data. It only becomes real-world evidence when it’s complete, high-quality, appropriately analyzed, and carefully interpreted. “It’s not about blindly trusting data from EMRs or claims. You need a quality system that ensures plausibility, consistency, and clinical relevance.”
Yes, the FDA accepts RWE, but with conditions. The agency has used RWE in label expansions and post-market safety monitoring (notably in pharmacovigilance through the Sentinel system). But they expect rigor: documented data provenance, statistical integrity, and early engagement with the agency. “Real-world data analysis is fast—but only if it’s done right.”
The source matters. Use caution with patient-reported or self-reported data. While useful for certain endpoints, patient surveys (especially around nutrition, behavior, or subjective symptoms) are prone to bias and fatigue. "Patients systematically underestimate their caloric intake in self-reports. And even registries create a pseudo-study environment that can influence behavior."
AI holds massive potential, but only when properly trained and contextualized. AI’s value lies in its ability to detect non-obvious patterns across enormous data sets. He cites one AI-enabled project that identified pancreatic cancer risk a full year before clinical diagnosis. But poorly trained models will simply amplify existing biases. “Garbage in, garbage out.”
We must be wary of mistaking statistical significance for clinical relevance. Large data sets can yield statistically significant results that don’t actually matter clinically. Stapff urges professionals to always ask: “Would this tiny difference actually change patient care?” If not, it may not be meaningful despite a low p-value.
Correlation ≠ causation. Even professionals get this wrong. Two trends moving in the same direction do not necessarily mean one causes the other. “Even in peer-reviewed journals, I see causality inferred from simple correlation. That’s dangerous.”
A scientifically sound pathway to knowledge must replace ‘belief.’ Stapff warns against confusing viral repetition with truth. He outlines a rational progression from anecdote → hypothesis → experiment → validation in the real world. “Facts repeated on social media aren’t evidence. You need a plausible mechanism and a consistent body of supporting data.”
AI isn’t done, and it needs human oversight. While AI can rapidly process and synthesize complex signals from across datasets, it’s still shaped by its training data. Users must engage multiple models, understand the limitations, and apply domain expertise to interpret results. “AI behaves like a human—just much faster, and based on more information.”
Some final advice from Dr. Stapff: Don’t fall into the trap of viewing RWE or AI as silver bullets. Use them to enhance—not replace—your evidence development processes. And above all, remember that scientific progress depends on educated, critical thinkers—not just tools. “It’s on us to keep democracy and science alive by being thoughtful consumers of information.”
Dr. Manfred Stapff is the founder and principal consultant at a boutique advisory firm focused on real-world evidence strategy, clinical development, and medical data intelligence. He is the author of Real World Evidence Unveiled and a frequent speaker on the role of data integrity, statistical literacy, and AI in advancing medical research. His current work supports life science companies and investors in evaluating drug development strategies, acquisition opportunities, and data-driven innovations.
Connect with him on LinkedIn here.
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