Dr.MetabolicMD said:

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

Dr.MetabolicMD 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 statins for secondary prevention. That's a strong clinical argument for this approach.

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

How would you tell the difference between that and the alternative explanation?

dan_philly said:

Dr.MetabolicMD said: ...regarding the trial evidence...

Forest plot interpretation for the the trial evidence meta-analysis: when reading the pooled estimate, pay attention to:

1. Point estimate (HR/RR/OR) — center of the diamond
2. Confidence interval width — precision of the estimate
3. I² statistic — heterogeneity across studies
4. Individual study weights — are results driven by one large trial?
5. Prediction interval — range of plausible true effects in future settings

The the trial evidence meta-analysis shows a pooled RR of 0.83 (95% CI 0.72-0.83), I²=41%. This is a robust and consistent effect.

Reporting back.

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.

Dr.ObesityMed said:

Forest plot interpretation for the the trial evidence meta-analysis: when reading the pooled estimate, pay attention to: Point estimate (HR/RR/OR) —…

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 25,000 GLP-1 users vs matched controls showed reduced all-cause mortality (HR 0.81) over 5 years of follow-up[1].

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