GenomicsKate said:

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

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 0.67-0.86), I²=54%. This is a robust and consistent effect.

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

Did your prescriber agree with that reading, and if not what was their objection?

NicoleRaleigh said:

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

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.

NurseLeah_Nash 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 25,000 GLP-1 users vs matched controls showed reduced stroke risk (HR 0.82) over 5 years of follow-up[1].

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