Collecting this in one place because it comes up every few weeks and the answer is always assembled from scratch. It is about the trial evidence, and it is deliberately narrow — everything I am not confident about is marked as such.
What is actually established
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.
The condition it depends on
Subgroup analyses deserve particular suspicion. With enough subgroups something is significant by chance, and pre-registered subgroups are a different animal from ones found afterwards.
The practical version
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.
What I am not sure about
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.
lori_vegas said:Relative and absolute effects need reading together.
lori_vegas has the substance of this right. The condition it depends on is worth stating. 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.
lori_vegas said:Relative and absolute effects need reading together.
This is where I part company with the consensus forming above. I would add the less popular caveat: these trial populations under-represented several groups, older adults and the highest BMI categories among them. The results probably generalise, and "probably" should be stated as an assumption rather than dropped.
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Browse GL BiochemAnswering 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.
MikeFit_NJ said:The gap between trial results and real-world results is consistent and it is not fraud.
Mine went the same way, slower. Posting only so the count is not one.