Relatively Impressive – Absolutely Disappointing?
When you hear that a new drug reduces the risk of a heart attack by 50%, are you impressed? Probably. But in reality, you’re only being shown part of the whole picture.
In evidence-based medicine, it’s not only what is said that matters, but also how it is said. A relative risk reduction (RRR) of 50% sounds dramatic and is a classic marketing tactic for medical products. But the RRR is only half the truth.
An example:
In a randomized trial, a new drug to prevent heart attacks is tested. (Randomization means that participants are assigned by chance to either a treatment or control group, ensuring comparability and reducing bias).
In the control group, 2 out of 100 people (2%) suffer a heart attack.
In the treatment group, 1 out of 100 people (1%) suffers a heart attack.
The statement that often follows: “50% risk reduction!”
True—relatively speaking. (1 out of 2 equals 50%).
But absolutely, the risk fell by just one percentage point: from 2% to 1%. Knowing the difference is crucial.
RRR vs ARR vs NNT – The Hard Truth in Three Numbers
RRR (Relative Risk Reduction):
→ here: 50%
RRR tells you by what percentage the risk of an outcome (e.g., disease) in the treatment group (1%) is reduced compared to the control group (2%).ARR (Absolute Risk Reduction):
→ here: 1%
ARR shows the actual difference in event rates between the control group and the treatment group.NNT (Number Needed to Treat):
How many patients must be treated for one to benefit?
→ NNT = 1 / ARR → 100
This may sound like semantics, but it has direct practical relevance. Only absolute risk reduction allows us to calculate the NNT—the number of patients who must be treated to prevent a single event. In this example, the NNT is 100: one in 100 patients benefits from the treatment, while the other 99 gain no measurable advantage (and may even face side effects or costs).
What Matters Is Context and Truth—Not the Wow Factor
Next time you hear about a “dramatic” risk reduction, ask yourself:
How strong was the effect in absolute terms?
How many people need treatment for one to benefit (NNT)?
What are the side effects or opportunity costs of this intervention?
What is the baseline risk or prevalence of the condition?
That’s how we separate scientific evidence from marketing spin.
Other Traps to Watch Out For
Truncated Y-Axes: Small Effects, Big Illusions
Another common trick is shortening the Y-axis in bar charts. If the axis doesn’t start at zero but at, say, 50, even tiny differences suddenly look dramatic. A difference of just a few percentage points can be made to look huge. Such visualizations are misleading, especially without context.
Image caption suggestion:
Two charts with identical values but different Y-axis scales. On the left, the difference looks modest. On the right, it looks dramatic. The shortened axis creates visual distortion and significantly changes interpretation—a classic example of how graphic design can shape, or manipulate, perception.
Surrogate Parameters – Real Effect or Just Cosmetic Numbers?
Finally, check whether studies measure real outcomes or only surrogate markers. A surrogate is a proxy variable—such as blood pressure, cholesterol, or a biomarker. They’re easier to measure and change quickly, but don’t always translate into meaningful benefits.
For example, a drug might lower blood pressure values without reducing stroke or death rates in the long run. A well-known case: intensive blood sugar lowering in type 2 diabetes. Many studies showed HbA1c values dropping significantly, but clinical benefits were absent—or even reversed with more side effects (e.g., ACCORD trial, NEJM 2008). A “better” lab number didn’t mean a better life.
Conclusion: Truth Needs Context
The more familiar you become with these nuances, the better you can interpret medical information. Good medicine isn’t about flashy numbers but about understanding what they really mean.
This isn’t about discrediting research, drugs, or science. It’s about paying attention to the details. At YEARS, we place great importance on distinguishing between what truly makes a difference and what only looks good on paper.
It’s about professionalism, precision, and separating signal from noise.
That’s also why we believe raw data alone isn’t enough. Data needs to be understood, put into context, and carefully interpreted. And that’s exactly what we do at YEARS. With us, you always discuss your results directly with a physician—not in a rushed five- or ten-minute slot, but with the time needed to make smart decisions together.
In our entry program, YEARS Core, you’ll have several detailed consultations with your physician on the test day alone—covering medical history, supplement protocols, and results. Our doctors remain available for follow-up questions, and a thorough final discussion takes place in the following weeks, usually online for maximum flexibility.
In our extended programs, YEARS Evolve and YEARS Ultimate, we add concierge service and follow-up testing, ensuring that your specific questions on lab values, current studies, medications, or training protocols are answered in depth—without gimmicks or pseudoscience, but with real evidence.
References (selection):
Gigerenzer G, Edwards A. Simple tools for understanding risks: from innumeracy to insight.
Nuovo J, Melnikow J, Chang D. Reporting Number Needed to Treat and Absolute Risk Reduction in Randomized Controlled Trials. JAMA. 2002.
Cairo A. The Functional Art: An Introduction to Information Graphics and Visualization. New Riders; 2012.
ACCORD Study Group. Effects of Intensive Glucose Lowering in Type 2 Diabetes. NEJM. 2008;358(24):2545–2559.