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.
What I am after is how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
I would rather have one careful answer than five confident ones.
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.
MikeFit_NJ said:Relative and absolute effects need reading together.
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.
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Shop Reference Standardsjosh_phd_bmore 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…
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.
josh_phd_bmore said:...regarding the trial evidence...
I think this is an underappreciated point. To expand on it with some data:
A recent meta-analysis of 18 RCTs (n=15,600) found that the trial evidence was associated with a robust effect size across diverse patient populations[1].
The NNT was 15, which is comparable to metformin for T2DM prevention. That's a strong clinical argument for this approach.