by James Lyons-Weiler, PhD, Popular Rationalism, ©2026
(May 1, 2026) — We’ve seen it a thousand times. When pharma decides it wants to open a new market, it makes mountains out of molehills on efficacy. When that market is threatened by measured risk detectable as such, they start digging to bury it. Popular Rationalism has definitive, cited, in-depth articles on this widespread unethical practice – and calls to action on what we can all do about it to help end the abuse of science in the name of profit.
In 2022, a large clinical trial found that a widely-used statin reduced heart attacks by 36 percent in the study population. That number led the press releases. It appeared in headlines across every major news outlet. It was cited in physician discussions for the next two years.
The number that wasn’t in the headline: in absolute terms, 1.8 percent of the placebo group had a heart attack during the trial, compared to 1.1 percent of the treatment group. The 36 percent is a relative risk reduction. The actual reduction in your personal probability of a heart attack, if you are similar to the people in that trial, was 0.7 percentage points.
That is not a scandal. Relative risk reduction is a legitimate statistical measure. But it is a different measure than absolute risk reduction, and the two numbers tell very different stories about how much a drug is likely to help you. One of them appeared in the news. The other appeared in Table 2 of the supplementary materials.
This is the number they never put in the headline. And it is not an accident.
Why this happens — and why it keeps happening
Clinical trials are expensive. They are funded, in the majority of cases, by the companies whose products they are testing. The researchers who design trials are not fraudsters — most are doing careful, legitimate science. But the choices made during design, analysis, and reporting are not neutral. They are made by humans with careers, funding relationships, and institutional pressures. Those pressures do not produce falsified data. They produce choices.
The choice between reporting relative and absolute risk reduction is one such choice. Relative risk reduction almost always produces a larger, more impressive number. It is not wrong to report it. It is, however, common to report only it — which means the reader gets one frame, not the full picture.
The same dynamic appears across the most common features of health research: which outcome was chosen as the primary endpoint, how the control group was assembled, how long the follow-up ran, which subgroups were analyzed and which were not, which adverse events made the published table. None of these choices are invisible. They are all documented in the methods section. They are almost never in the abstract that gets summarized in the news.
The abstract is the press release. The methods section is the trial. Those are different documents, and they answer different questions.
The four questions that change everything
You do not need a PhD to read a clinical trial critically. You need four questions. These are not original to this publication — they are the standard tools of evidence-based medicine, published in peer-reviewed methodology literature. But they are almost never explained to the people who most need them: the patients, families, practitioners, and citizens who will live inside the decisions these trials produce.
Question 1: What was the absolute risk reduction?
Take the event rate in the control group and subtract the event rate in the treatment group. That number — not the relative reduction — tells you how much your actual risk changed. In many trials of widely-used drugs, this number is under 1 percent. Knowing it does not mean the drug is useless. It means you can make an informed decision about whether the benefit is worth the cost, the side effects, and the inconvenience.
Question 2: How was the control group constructed?
The ideal control group is a random sample of people identical to the treatment group in every way except the intervention being tested. In practice, many trials use active controls — comparing a new drug to an existing drug rather than to placebo — or use surrogate outcomes, measuring a biomarker rather than the clinical event that actually matters, or follow participants for periods too short to capture long-term effects. None of these are automatically disqualifying. But they change what the trial can and cannot tell you.
Question 3: Who funded it, and who analyzed it?
Industry-funded trials are more likely to report positive outcomes than independently funded trials. This is documented in the peer-reviewed literature — it is not a claim about fraud, it is a finding about the cumulative effect of design choices, publication decisions, and analysis framing. Knowing the funding source does not tell you the trial is wrong. It tells you to look more carefully at the design choices listed above.
Question 4: What happened to the people who left?
Dropout rates in clinical trials are often substantial. In trials of psychoactive medications, behavioral interventions, and dietary programs, many participants stop completing the protocol before the trial ends. How those dropouts are handled in the final analysis changes the result. If people who dropped out because of side effects are excluded from the adverse event calculation, the drug looks safer than it was. The methods section says how dropouts were handled. The abstract almost never does.
Read the rest here.