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.
A quick sanity check on any figure quoted here: is it mean or median, is it intention-to-treat or completers, and what was the comparator. Three questions, and they resolve most disagreements in these threads.
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.
Practical detail welcome, however dull — the duller the better.
This one has a reasonably settled answer, so here it is. 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.
BariatricNurseD said:Relative and absolute effects need reading together.
BariatricNurseD 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 statins for secondary prevention. That's a strong clinical argument for this approach.
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View ResultsJenMemphis 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.
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.