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In biopharma, statistical significance is often treated as a green light. A p-value below 0.05 can unlock the next phase of development, trigger regulatory submissions, and even generate early buzz in the marketplace. But as anyone in this industry long enough knows—statistical significance is a necessary condition, not a sufficient one. The real question we should be asking is and increasingly do ask is: Is this result clinically meaningful?
This very topic emerged in a conversation with a client earlier this week. We were discussing the perennial question that is posed to respondents in new products marketing research studies: What level of improvement over standard of care is truly enough to change behavior? It’s a deceptively simple ask—one we pose all the time in new products research—and yet, it strikes at the core of how we define value. I’ve heard this same challenge echoed by clients across functions, from commercial leads to medical affairs. Because in this business, success isn’t just about statistical wins—it’s about turning data into decisions. And that translation depends on understanding what stakeholders—physicians, patients, payers—actually perceive as meaningful enough to act on.
Statistical significance tells us whether an observed result is unlikely to have occurred by chance, based on a pre-set threshold (usually p < 0.05). It’s about mathematics, not meaning. Clinical meaningfulness, on the other hand, is about tangible impact. It asks:
It’s possible to have a statistically significant result that is functionally irrelevant—or to have a clinically compelling result that doesn’t quite reach traditional significance because of sample size, measurement noise, or design limitations.
And therein lies the challenge.
The gap between “statistically significant” and “clinically meaningful” often emerges during the translation phase—when clinical data is handed over to commercial teams to shape positioning, forecast uptake, or develop launch strategy.
Take HCP feedback from the early-phase marketing research study. Clinicians often might say they expect at least a 10–15% improvement over standard of care to consider switching to a new treatment option. But that’s rarely a hard threshold. It's a directional signal—shaped by therapeutic area, disease severity, unmet need, and real-world practice constraints. A 10-15% delta might be transformative in oncology but barely noticeable in a chronic disease context.
The ambiguity creates friction. Clinical teams speak in endpoints; commercial teams speak in adoption curves. We need a common language—and better tools to define what “clinically meaningful” really means.
Having spent the last three decades sitting at the junction of clinical and commercial strategy, I’ve found that the most productive conversations start not with “Is this statistically significant?” but with “What would have to be true for this to matter in the real world?”
Here’s how marketing research can help answer that question—both qualitatively and quantitatively:
1. Elicit Authentic, Real-World Relevance Through In-Depth Listening
Qualitative methods are often under-leveraged in this space. But they’re critical for unpacking the why behind decision-making.
In my experience, the most valuable insights often come when we stop asking about percentages and start asking about first-person stories and authentic narratives. When does a treatment change the tone of the consultation? Reduce calls from anxious caregivers? Make Monday mornings easier in the clinic?
2. Quantify the Thresholds That Actually Matter
Quantitative methods can then translate those qualitative learnings into structured, scalable insights.
These tools don’t give us the answer. But they help us define a range of acceptable answers—anchored in actual decision-making behavior.
3. Account for the Broader Value Context: Making the Abstract Tangible
A key lesson from commercializing drugs across multiple therapeutic areas: “Clinically meaningful” isn’t just about the molecule. It’s about the full experience. I wrote about this previously in my 'Ultimate Treatment Experience' piece.
To make progress, we need to ground clinical meaningfulness in the contexts that matter:
This is where behavioral science comes in. We know that people don’t always make decisions based on pure data. They’re influenced by framing, habits, and heuristics. Marketing research—done right—can identify those mental shortcuts and show how they interact with clinical data.
For example, using digital ethnography or live patient video diaries, we can explore the emotional and practical significance of a product’s impact. Does it eliminate a frustrating routine? Create more independence? Reduce fear?
And let’s be candid—perception matters. A statistically significant benefit can be dismissed if it's not well-articulated. Conversely, a modest benefit, if communicated with emotional clarity and contextual depth, can be seen as meaningful—even transformative.
One of the most frustrating aspects of this work is that “clinically meaningful” is often an implicit judgment—not something stakeholders can consistently define or agree upon. Even physicians may struggle to explain what level of improvement is truly persuasive. Patients often only recognize meaningful change after they’ve experienced it. And by then, the window to influence trial design may have closed.
Complicating matters further, each stakeholder brings a different lens. A payer might focus on reduced hospitalizations or long-term cost offsets. A caregiver might prioritize better sleep or fewer emergency calls. A physician may look for ease of integration into current workflows or faster symptom resolution. And a patient may simply want to regain independence or avoid stigma in daily life.
These aren’t soft metrics—they’re real-world priorities. And no single p-value can capture the full range of what makes a treatment feel truly worthwhile.
Ultimately, the question “Is it clinically meaningful?” is about alignment—between evidence and experience, numbers and narratives, expectations and outcomes.
Marketing research has a unique responsibility here. Not just to test messages, but to help define what deserves to be messaged. To shape the scientific narrative early—so that what gets measured is what matters.
Because in a noisy market, it’s not enough to prove a product works. You have to prove it matters.
And that’s where commercial teams—armed with better listening, sharper modeling, and an obsession with real-world impact—can lead the way.
