One concrete data point for the thread. A quick sanity check on any figure quoted here: is it mean or median, is it intention-to-treat or completers, and what was the comparator. Three questions, and they resolve most disagreements in these threads.
A narrower follow-up, since the general answer is now clear:
How to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases?
NicoleRaleigh said:A quick sanity check on any figure quoted here: is it mean or median, is it intention-to-treat or completers, and what was the comparator.
Adding the part of the answer the thread has not reached. Relative and absolute effects need reading together. A 20% relative reduction on a high baseline risk is a large absolute benefit; the same relative figure on a low baseline risk is a small one, and press summaries almost always quote the relative number because it is bigger.
Community-driven peptide testing and vendor rating platform. Transparent results. Unbiased analysis. Trusted by thousands.
View ResultsOP 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.
Dr.MetabolicMD said:Relative and absolute effects need reading together.
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.