lori_vegas said:

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

lori_vegas 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 antihypertensives for stroke reduction. That's a strong clinical argument for this approach.

A narrower follow-up, since the general answer is now clear:

What did you change at the same time, and can you separate the two now?

SleepDoc_PDX said:

lori_vegas said: ...regarding the trial evidence...

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 18,000 GLP-1 users vs matched controls showed reduced stroke risk (HR 0.82) over 4 years of follow-up[1].

These results complement the RCT data and suggest the benefits translate to real-world populations.

OP back with an update, since a thread like this is useless without one.

Update — my curve sits below the published mean and the explanation is that the trial arm had support I do not have. That was reassuring rather than otherwise.

CarlaRPh_TPA said:

Propensity score matching studies and the trial evidence: when RCTs aren't available for a specific question, propensity score-matched observational…

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.