Dr.RenalNash said:

Relative and absolute effects need reading together.

Pushing back on Dr.RenalNash here. 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.

Dr.ObesityMed 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…

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 0.86 (95% CI 0.71-0.86), I²=44%. This is a robust and consistent effect.

LipidDoc_ATL 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 18,000 GLP-1 users vs matched controls showed reduced heart failure hospitalization (HR 0.74) over 4 years of follow-up[1].

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

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

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

Closing the loop on my own question.

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.