Dr.RenalNash said:Read four things before the headline number.
This is exactly what I could not find anywhere else.
Adding the clinical framing, because it changes how the question reads.
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.
LindaRN_retired said:The liver data is among the strongest non-weight findings in the class.
LindaRN_retired 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 metformin for T2DM prevention. That's a strong clinical argument for this approach.
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Shop Reference StandardsMASHdoc_SA said:Relative and absolute effects need reading together.
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 all-cause mortality (HR 0.81) over 3 years of follow-up[1].
These results complement the RCT data and suggest the benefits translate to real-world populations.
Moderator note: good thread. Keeping it here rather than moving it, because the question is general enough to be useful. Report rather than reply if it drifts again.