The Evidence Was There. So Why Didn’t It Matter?
Moderator
Luis Prieto, Novartis, Basel, Switzerland
Speakers
Mona Khalid, Adigens Health, Dublin, Ireland; Clark Paramore, MSPH, Biogen, Cambridge, MA, United States; Miguel Hernán, Harvard T.H. Chan School of Public Health, Boston, MA, United States
ISSUE:
Not all rigorous evidence is influential. Despite advances in methods like target trial emulation, quantitative bias analysis, and synthetic control arms, many high-quality real-world evidence (RWE) submissions still fail to sway decision-makers.
Why? Because methodological rigor, while essential, is not sufficient. In many cases, the underlying data are not “fit for purpose” and no amount of statistical sophistication can overcome missing clinical nuance, misclassification rooted in data capture, or unmeasured confounding beyond the reach of adjustment. Meanwhile, subtle but powerful dynamics (e.g., institutional familiarity, the perceived credibility, and the comfort of precedent) often shape how evidence is interpreted and used.
This panel explores the disconnect between scientific quality and decision impact, asking whether technical excellence is being filtered through institutional and human systems in ways that are rarely made explicit.
OVERVIEW:
This session will unpack both technical and institutional factors that affect the impact of RWE in regulatory and HTA decision-making. After a short framing by the moderator, speakers will discuss and debate the following:
• Why statistically sound approaches fail when the underlying data lack clinical validity or completeness.
• Whether there is a credibility penalty for unfamiliar methods, even when transparent and well-documented.
• If early engagement with regulators/HTAs, co-development with academic partners, or third-party validation can bridge the trust gap.
• What is needed to address the “below-the-surface” data quality issues that continue to limit the RWE enterprise.
Drawing on real case studies across Europe and North America, the panel will examine where even rigorous methods have fallen short and what lessons can be learned.
Code
037
Topic
Health Technology Assessment, Methodological & Statistical Research, Patient-Centered Research