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A few weeks ago, during an interview with an infectious disease physician about the current state of HIV treatment, I heard a remark that struck me as both clinically insightful and psychologically revealing. Reflecting on how antiretroviral therapy had evolved over his career, he explained that for many years, clinicians assumed there was always a trade-off between efficacy and tolerability. If a regimen was highly potent, it must be harder to tolerate. If it was gentler on patients, it must have a low barrier to resistance.
To him, and to most HIV-treating HCPs practicing through the late 1990s into the early 2000s, this wasn’t merely a hypothesis. It was lived experience. During that era, NNRTIs (non-nucleoside reverse transcriptase inhibitors) were generally well-tolerated and easier to take, but vulnerable to rapid resistance and virologic breakthrough if not taken consistently. Efavirenz was case in point. Meanwhile, PIs (protease inhibitors) offered higher barriers to resistance but came with metabolic complications, lipodystrophy, drug–drug interactions, and challenging tolerability profiles -- especially in the early ritonavir-boosted formulations.
In other words, the field was steeped in an implicit logic: efficacy lives on one side of the see-saw, tolerability on the other. Clinicians internalized this trade-off not just as a historical observation but as a clinical law of nature.
Then came the disruptor.
With the advent of integrase strand transfer inhibitors (INSTIs) -- first raltegravir, then dolutegravir and bictegravir -- HCPs witnessed something that contradicted decades of expectations. Suddenly, regimens were emerging that delivered high potency, high tolerability, and high forgiveness, with robust resistance barriers when used in combination therapy. The see-saw didn’t just balance; it broke.
What happened in HIV is instructive far beyond the category. It illustrates a cognitive phenomenon I’ve long referred to as the “trade-off bias” -- an intrinsic belief that clinical attributes exist in zero-sum tension, and that gains in one domain must be offset by losses in another. Understanding this bias is essential for anyone conducting marketing research, shaping positioning, or launching therapies into competitive markets.
Although I’ve utilized the phase “trade-off bias” in my research and consulting work, the phenomenon is well documented across behavioral science under labels such as zero-sum bias, compensatory reasoning, and the assumption of necessary trade-offs. In consumer psychology, this is known as the “too good to be true” effect. In organizational behavior, it appears as the belief that constrained resources must be balanced by symmetrical outcomes.
So why does this particular bias show up so reliably in healthcare?
Medical decision-making, despite being evidence-based, is also pattern-based. When a pattern holds for long enough (e.g., “potent drugs tend to have more side effects”), it becomes a mental model. New evidence must work harder to displace old experience.
When the consequences of being wrong are significant/serious, people err on the side of skepticism. A claim of “high efficacy and high tolerability” is treated with caution because it seems to violate the equilibrium that HCPs expect.
The human brain naturally prefers stories where strengths are balanced by weaknesses. A profile that seems “unbalanced” can trigger cognitive dissonance.
HIV therapy is one prime example, but similar patterns exist in oncology, autoimmune disease, psychiatry, and pain management. Once a category establishes a trade-off narrative, it becomes sticky.
Understanding the bias is not enough. For insights professionals, the key is to detect it early and accurately.
Depth interviews, ethnographies, and quantitative studies all offer the opportunity to surface subtle markers of trade-off bias, but they require careful interpretation. Here are the patterns I’ve found most diagnostic:
If respondents repeatedly ask, “How is that possible?” or “What’s the catch?”, this signals that the profile contradicts their mental model. Clinicians are not expressing disbelief in the evidence; they’re expressing disbelief in the pattern.
In DCM, attribute combinations that should dominate may perform unexpectedly poorly early in the research process. This often reflects a bias against profiles that feel unrealistic rather than a true preference for inferior options.
You describe a new antidepressant with both a favorable side-effect profile and strong, rapid symptom improvement, and the psychiatrist replies, “If patients aren’t experiencing many side effects, I’d wonder whether the therapeutic effect is really as robust as it seems.”
This instinctive assumption -- that better tolerability must signal weaker clinical impact -- is a classic example of compensatory reasoning in action.
Listen for phrases like:
When HCPs say, “I need to see more real-world data,” it can reflect legitimate caution; but it can also indicate discomfort with abandoning an entrenched mental model.
Identifying the bias is crucial because failure to do so can lead to misinterpretations. Without this interpretive lens, insights teams may conclude that a product’s value proposition is unclear or unappealing -- when in reality the issue is that it feels too good to be true.
The INSTI example shows that trade-off biases can be broken, but usually only when several conditions align:
New MOAs, for example: CAR-T, RNA therapeutics, antibody-drug conjugates, signal to clinicians that the old rules may no longer apply.
INSTIs didn’t rely on a single strong data point. Across trials, new additions to the class, and usage across diverse populations, the pattern held.
Clinicians must see the absence of trade-offs in real practice before they fully internalize it.
Commercial and marketing teams can accelerate this process by shaping the message architecture around breaking the trade-off narrative.
When addressed intentionally, this bias can shift from a natural headwind to a meaningful tailwind. Some strategies:
The most powerful messaging pathways often begin with: “For years, clinicians have had to choose between X and Y. Now, emerging data show that may no longer be necessary.” This mirrors the clinician’s own journey and avoids triggering skepticism.
Data alone may not break a bias. Mechanistic explanations, e.g., MOA logic, target specificity, biomarker-guided dosing, etc. help clinicians understand why the trade-off doesn’t apply.
For example:
Sometimes the best approach is to reposition the therapy as part of a new paradigm rather than a competitor within the old framework. This is how INSTIs reframed HIV treatment.
Breaking a bias requires coordinated reinforcement across sales, MSL encounters, digital content, peer-to-peer programs, and conference presence. Consistent storytelling is essential.
Every therapeutic area has historical trade-offs that shape clinician expectations: efficacy vs. safety, speed vs. durability, potency vs. convenience, biologic precision vs. cost. When your product disrupts one of these narratives, you are not only competing on clinical attributes; you are competing against a cognitive schema.
Recognizing this changes the work of commercial leaders. The challenge becomes:
How do we help the field see that the old trade-off is no longer a natural law?
The answer lies in the intersection of insights, behavioral science, and strategic communication.
The HIV story is powerful because it shows how deeply clinicians internalize trade-offs; and how transformative it can be when those trade-offs vanish. The trade-off bias is not a flaw; it is an adaptive psychological mechanism built on years of real clinical experience. But innovation often outpaces expectation, and when it does, the greatest commercial opportunity lies not only in presenting new data, but in reshaping the mental models that clinicians bring to the table.
When your brand enables the field to believe, credibly and confidently, that you can have it both ways, you aren’t just winning market share -- you’re redefining the category.
