The figures, for anyone assembling their own picture. 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?
DanielChem_CHI 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.
There is a second half to this that has not been said yet. Read four things before the headline number. The population, because trial populations are selected and supported in ways that real cohorts are not. The comparator, because "better than placebo" and "better than the current standard" are different claims and get reported identically. The primary endpoint as pre-registered, because a secondary endpoint promoted after the fact is a hypothesis rather than a finding. And the completion rate, because a large effect in the half of participants who finished is a different result from a large effect in everybody enrolled.
I would rather be corrected than agreed with, if it comes to it.
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.
claudia_zurich said:Read four things before the headline number.
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.