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 bit I cannot resolve on my own is how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
Not looking for reassurance. Looking for the part I have got wrong.
This one has a reasonably settled answer, so here it is. 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.
RetaRick_CA 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.
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View ResultsEndoResFellow 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.
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 heart failure hospitalization (HR 0.74) over 3 years of follow-up[1].
These results complement the RCT data and suggest the benefits translate to real-world populations.