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.
My own curve sits about four points below the published mean and I spent two months assuming that meant something was wrong with me or with my material.
So the question, as narrowly as I can put it: how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
Numbers rather than impressions, if you have them.
Dr.SportsMedIN said:Read four things before the headline number.
Dr.SportsMedIN said:...regarding the trial evidence...
I think this is an underappreciated point. To expand on it with some data:
A recent meta-analysis of 22 RCTs (n=18,900) found that the trial evidence was associated with a consistent effect size across diverse patient populations[1].
The NNT was 20, which is comparable to metformin for T2DM prevention. That's a strong clinical argument for this approach.
Sigma-Aldrich — Research-Grade Standards
Certified reference materials, analytical reagents, and research-grade standards for peptide verification. Trusted by laboratories worldwide.
Shop Reference StandardsFitDadDave said:My own curve sits about four points below the published mean and I spent two months assuming that meant something was wrong with me or with my…
Same position here, arrived at the long way round. 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.
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 12,000 GLP-1 users vs matched controls showed reduced MI incidence (HR 0.78) over 3 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.