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.

SarahChen_PharmD said:

The gap between trial results and real-world results is consistent and it is not fraud.

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.

DadBodDave 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 DadBodDave 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.

That is the short version; the long version is somebody else's post.

Clinical perspective, offered as context rather than as advice.

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.