One concrete data point for the thread. 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.
One thing that is still open after EndoResFellow’s answer:
How to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases?
labquiet_amy said: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.
There is a second half to this that has not been said yet. 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.
That is the short version; the long version is somebody else's post.
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Shop Reference StandardsClosing the loop on my own question.
Follow-up: I read the paper rather than the summary and the qualifier I was missing was in the second paragraph of the results.
VendorMark said:The gap between trial results and real-world results is consistent and it is not fraud.
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.