Taking the question as asked, rather than the general version of it. 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.
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.
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.
The question I want answered is how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
If the honest answer is that nobody knows, that is a useful answer and I would rather have it.
LibrarianMeg 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.
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View Resultsnick_newbie 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…
This matches mine closely enough to be worth saying so. 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.
From the other side of the consultation, briefly.
nick_newbie 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 antihypertensives for stroke reduction. That's a strong clinical argument for this approach.