I keep finding that the number in the press summary and the number in the paper are not the same number, and the difference is always in the same direction.
What I am trying to establish is how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
Practical detail welcome, however dull — the duller the better.
Taking the question as asked, rather than the general version of it. Relative and absolute effects need reading together. A 20% relative reduction on a high baseline risk is a large absolute benefit; the same relative figure on a low baseline risk is a small one, and press summaries almost always quote the relative number because it is bigger.
If somebody has the primary source to hand I would rather cite it than paraphrase it.
Dr.RenalNash said:Relative and absolute effects need reading together.
Dr.RenalNash said:...regarding the trial evidence...
I think this is an underappreciated point. To expand on it with some data:
A recent meta-analysis of 12 RCTs (n=8,400) found that the trial evidence was associated with a significant effect size across diverse patient populations[1].
The NNT was 8, which is comparable to antihypertensives for stroke reduction. That's a strong clinical argument for this approach.
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View ResultsDr.GastroMayo said:I keep finding that the number in the press summary and the number in the paper are not the same number, and the difference is always in the same…
This matches mine closely enough to be worth saying so. The gap between trial results and real-world results is consistent and it is not fraud. Trial participants get titration by protocol, scheduled contact, free drug and dietetic support; removing that infrastructure costs a few percentage points every time it has been measured. When your own curve sits below the published mean, that is the likeliest explanation before anything about you or your material.
Adding the clinical framing, because it changes how the question reads.
Bayesian meta-analysis perspective on the trial evidence: traditional frequentist meta-analyses report point estimates and confidence intervals. Bayesian approaches provide probability distributions that are more intuitive for clinical decision-making.
For example: "There is a 98.5% probability that semaglutide 2.4mg produces >10% weight loss vs placebo" is more actionable than "RR 3.4, 95% CI 2.8-4.1, p<0.001."
The the trial evidence evidence is strong under both frameworks, but Bayesian analysis better communicates the degree of certainty for individual patient counseling.