My own curve sits about four points below the published mean and I spent two months assuming that meant something was wrong with me or with my material.
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.
The narrow version of the question is how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
If the honest answer is that nobody knows, that is a useful answer and I would rather have it.
Answering the narrow version, because the broad one does not have a single answer. 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.
DataDave said:Relative and absolute effects need reading together.
Forest plot interpretation for the the trial evidence meta-analysis: when reading the pooled estimate, pay attention to:
The the trial evidence meta-analysis shows a pooled RR of 0.77 (95% CI 0.70-0.87), I²=35%. This is a robust and consistent effect.
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View ResultsSaraMom3 said:My own curve sits about four points below the published mean and I spent two months assuming that meant something was wrong with me or with my…
Can confirm the pattern SaraMom3 describes. The gap between trial results and real-world results is consistent and it is not fraud. Trial participants get titration by protocol, scheduled contact, free drug and dietetic support; removing that infrastructure costs a few percentage points every time it has been measured. When your own curve sits below the published mean, that is the likeliest explanation before anything about you or your material.
Happy to go further on any of that.
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.