Reading the Clinical Paper Commercially:
Jack Kohler’s Methods to Bridge from Evidence to Strategy
The essay below is written by Chat GPT 5.6 after reading Kohler's web articles. He's focused on biopharma, where one main pivotal trial usually is hugely influential in FDA decisions as well as coverage. In diagnostics, VERY often, at least tests in novel areas, a whole sequence of papers are required for coverage. E.g. see the lengthy clinical trial page for Naveris NavDx for MRD in H&N cancer. Here, here.
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Clinical papers are written to report research. Pharmaceutical companies must ask them to do something more: clarify which patients matter most, whether a product is meaningfully differentiated, what claims the evidence can support, and whether the result can become a credible positioning and value story.
Jack Kohler has built a consulting practice around that translation. His background spans pharmaceutical sales, brand management, and international marketing, and he describes his work as helping pharma and biotech teams turn clinical evidence into “decision-ready strategy.” (LinkedIn)
His recent infographic, “How to Read a Clinical Paper Commercially,” organizes the task into nine steps:
Research question
Population
Study design
Endpoints
Efficacy graphs
Forest plots and subgroups
Safety
Statistics and missing data
Commercial meaning
For each section, Kohler asks three things: What does it tell us? What should we watch for? What further question should we ask?
The value lies in the sequence
No individual box is revolutionary. Experienced readers already know to examine inclusion criteria, distinguish primary from exploratory endpoints, inspect confidence intervals, and look beyond the mere separation of Kaplan-Meier curves.
That is not much of a criticism, however. The value lies in assembling those observations into a disciplined sequence—and placing commercial meaning last rather than first.
A familiar failure in commercial organizations is to move immediately from “the trial was positive” to “what should our message be?” Kohler instead begins with what the study was designed to establish, who was actually studied, and how strong and durable the result was.
As he notes, two people can read the same paper and reach different commercial conclusions. The difference often lies beneath the headline: the enrolled population, endpoint hierarchy, clinical rather than merely statistical significance, location of the strongest signal, and trade-offs accepted to obtain the benefit. Those details can materially change patient prioritization, positioning, pathway fit, and the value story.
Trial success is not yet commercial strategy
Kohler’s illustrative Phase III example shows the distinction clearly.
The primary endpoint is met in the overall population. A higher-need subgroup appears to obtain greater benefit. Secondary outcomes are mixed, while tolerability and discontinuations deserve closer review.
Different functions naturally emphasize different parts of this result:
Clinical development leads with the successful primary endpoint.
Medical affairs asks what the evidence genuinely supports.
Market access asks where the value argument is most specific.
Commercial leadership must choose between a broad efficacy claim and a more focused patient position.
Kohler’s proposed commercial reading is to consider leading with the higher-need segment, where effect size, clinical relevance, and the access rationale may align most strongly. The overall result provides clinical credibility; the narrower population may provide commercial differentiation.
That is an important distinction. Clinical development asks, “Did the trial succeed?” Commercial strategy must also ask, “Which part of the result creates the strongest defensible reason to use this product?”
The answer may be narrower than the regulatory indication. It may also differ by audience. Physicians, payers, patients, and pharmacy committees can accept the same study while assigning very different value to its findings.
What to leave behind
One of Kohler’s best formulations is that commercial translation should determine what to lead with, what to qualify, and what to leave behind.
The last item may be the hardest.
Commercial teams are often tempted to collect every favorable endpoint, subgroup, and biomarker observation. But a long catalogue of modest findings can obscure the strongest argument. Conversely, an appealing subgroup can become commercially dominant even though it was exploratory, underpowered, or inconsistent with the overall result.
Good translation therefore requires an evidence hierarchy. Not every statistically interesting finding deserves strategic weight. Not every endpoint belongs in the value proposition. And not every potentially eligible patient belongs at the center of the initial commercial story.
This is where Kohler’s method rises above a checklist for reading papers. It asks the organization to make a decision about the relative weight of the evidence.
A few additions
The framework appropriately warns about post hoc subgroup analyses, statistical power, follow-up, missing data, censoring, and the difference between statistical and clinical significance.
A fuller commercial assessment would add several implementation questions:
Does the comparator represent current practice? Can the preferred patient segment be identified reliably in ordinary care? Would doing so require new testing or workflow? Does the intervention reduce costs, shift them, or add costs in exchange for benefit? Will the proposed story survive the regulatory label, guideline review, payer scrutiny, and real-world implementation?
These are not corrections to Kohler’s framework so much as the next stage of applying it.
More than “read the Kaplan-Meier curve”
There is a mild temptation to call this “stacking the obvious.” In places, it is. But obvious points are frequently applied incompletely or inconsistently—especially across departments.
A clinical scientist may understand the trial but have little responsibility for positioning. A brand team may know the desired position but be less comfortable with endpoint hierarchy or missing-data assumptions. A market-access team may identify the strongest payer argument only after the broader evidence narrative has hardened.
Kohler’s framework gives these groups a common route through the evidence:
What was demonstrated? Where is the evidence strongest? Where is it fragile? Which patients create the clearest clinical and commercial rationale? What should we say—and what should we resist saying?
The framework does not replace medical judgment, biostatistics, regulatory analysis, or payer research. Its contribution is to keep commercial interpretation attached to the architecture of the trial.
That is useful work. A successful primary endpoint can rapidly acquire layers of commercial meaning that the study never established. Kohler’s method imposes a healthier discipline: understand exactly what the evidence supports, identify what remains uncertain, and only then translate the result into positioning, patient priorities, pathway fit, and value.
Taken as a whole, it is a thoughtful and practical bridge from clinical publication to commercially credible strategy.
Plain-text links for copying
Jack Kohler’s website:
https://clinicalcommercial.com/
Illustrative clinical-to-commercial readout:
https://clinicalcommercial.com/#example
Jack Kohler on LinkedIn:
https://nz.linkedin.com/in/jack-kohler-70402491
