PharmD_Rodriguez said:

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

PharmD_Rodriguez said:

...regarding the trial evidence...

I think this is an underappreciated point. To expand on it with some data:

A recent meta-analysis of 18 RCTs (n=15,600) found that the trial evidence was associated with a robust effect size across diverse patient populations[1].

The NNT was 15, which is comparable to antihypertensives for stroke reduction. That's a strong clinical argument for this approach.

One thing that is still open after pat_auckland’s answer:

What would you measure differently if you were starting again?

dave_SLC said:

PharmD_Rodriguez said: ...regarding the trial evidence...

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.

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

Follow-up: I read the paper rather than the summary and the qualifier I was missing was in the second paragraph of the results.

LipidDoc_ATL said:

Bayesian meta-analysis perspective on the trial evidence: traditional frequentist meta-analyses report point estimates and confidence intervals.

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 MI incidence (HR 0.78) over 4 years of follow-up[1].

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