Short answer first, then the reasoning. 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 bit I cannot resolve on my own is 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.
NurseKim_ATL said:The gap between trial results and real-world results is consistent and it is not fraud.
NurseKim_ATL said:...regarding the trial evidence...
I think this is an underappreciated point. To expand on it with some data:
A recent meta-analysis of 12 RCTs (n=8,400) found that the trial evidence was associated with a significant effect size across diverse patient populations[1].
The NNT was 8, which is comparable to statins for secondary prevention. That's a strong clinical argument for this approach.
PeptideMeter — Independent Peptide Analytics
Community-driven peptide testing and vendor rating platform. Transparent results. Unbiased analysis. Trusted by thousands.
View ResultsDr.CardioMD 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…
Can confirm the pattern Dr.CardioMD describes. 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.
Adding the clinical framing, because it changes how the question reads.
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 heart failure hospitalization (HR 0.74) 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.