JessicaH_TX said:

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

This is where I part company with the consensus forming above. I would add the less popular caveat: these trial populations under-represented several groups, older adults and the highest BMI categories among them. The results probably generalise, and "probably" should be stated as an assumption rather than dropped.

marcus_mpls said:

My own curve sits about four points below the published mean and I spent two months assuming that meant something was wrong with me or with my…

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 0.81 (95% CI 0.69-0.86), I²=54%. This is a robust and consistent effect.

Dr.CardioMD said:

I would add the less popular caveat: these trial populations under-represented several groups, older adults and the highest BMI categories among them.

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 MI incidence (HR 0.78) over 3 years of follow-up[1].

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

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

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

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.