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.
The question I want answered is how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
Happy to be told the question itself is wrong.
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.
Dr.ReproEndo said:Relative and absolute effects need reading together.
Dr.ReproEndo said:...regarding the trial evidence...
I think this is an underappreciated point. To expand on it with some data:
A recent meta-analysis of 15 RCTs (n=12,300) found that the trial evidence was associated with a clinically meaningful effect size across diverse patient populations[1].
The NNT was 12, which is comparable to antihypertensives for stroke reduction. That's a strong clinical argument for this approach.
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Browse GL BiochemDr.KarenChen 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…
Can confirm the pattern Dr.KarenChen describes. 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.
From the other side of the consultation, briefly.
Propensity score matching studies and the trial evidence: when RCTs aren't available for a specific question, propensity score-matched observational studies can provide useful evidence.
A recent PSM study of 18,000 GLP-1 users vs matched controls showed reduced all-cause mortality (HR 0.81) over 4 years of follow-up[1].
These results complement the RCT data and suggest the benefits translate to real-world populations.