I keep finding that the number in the press summary and the number in the paper are not the same number, and the difference is always in the same direction.
So the question, as narrowly as I can put it: how to read a result like this without either dismissing it or over-reading it, since the summaries all read like press releases.
Tell me what I have not thought of.
Answering the narrow version, because the broad one does not have a single answer. 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.
Ask again with the specifics and you will get a better answer than this one.
Dr.PainCLE 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.
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View ResultsLarryQC_SD said:I keep finding that the number in the press summary and the number in the paper are not the same number, and the difference is always in the same…
This matches mine closely enough to be worth saying so. 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.
From the other side of the consultation, briefly.
LarryQC_SD 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.