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.
The question I want answered is how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
Numbers rather than impressions, if you have them.
Short answer first, then the reasoning. 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.
LipidDoc_ATL said:Relative and absolute effects need reading together.
LipidDoc_ATL 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 statins for secondary prevention. That's a strong clinical argument for this approach.
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Shop Reference StandardsTrialNerd_Beth 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…
Same position here, arrived at the long way round. 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.
Clinical perspective, offered as context rather than as advice.
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.