Short answer first, then the reasoning. Read four things before the headline number. The population, because trial populations are selected and supported in ways that real cohorts are not. The comparator, because "better than placebo" and "better than the current standard" are different claims and get reported identically. The primary endpoint as pre-registered, because a secondary endpoint promoted after the fact is a hypothesis rather than a finding. And the completion rate, because a large effect in the half of participants who finished is a different result from a large effect in everybody enrolled.
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 would genuinely help is knowing 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.
InsuranceTom said:Read four things before the headline number.
InsuranceTom 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 metformin for T2DM prevention. That's a strong clinical argument for this approach.
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Browse GL BiochemJenMemphis 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 JenMemphis describes. 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.
Adding the clinical framing, because it changes how the question reads.
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 25,000 GLP-1 users vs matched controls showed reduced all-cause mortality (HR 0.81) over 5 years of follow-up[1].
These results complement the RCT data and suggest the benefits translate to real-world populations.
[1] Registry-based cohort study, pre-print 2024.