Concretely: SURMOUNT-1 · vomiting.
The figures are clear enough; the question is what they mean and what they do not.
I can supply the numbers if the specifics change the answer.
What would I need in addition before this supported a decision?
The SURMOUNT-1 placebo arm is the only thing that makes its treatment arm interpretable, and it is the row nobody quotes. Symptoms reported under placebo in these programmes are not rare, because the population is being asked about them weekly and would have had some of them regardless. The attributable figure is the treated rate minus the placebo rate, and that difference is routinely a fraction of the headline. Two cautions on the subtraction: the arms must have been assessed the same way, and a discontinuation for an event removes that participant from later time points in both arms, which flatters whichever arm loses more people.
The honest answer here is that the published evidence supports part of the claim and is silent on the rest, and it is worth being precise about which part is which.
Placebo arms in this class are not nothing. Lifestyle-intervention placebo arms in the major obesity trials commonly lose two to three per cent of body weight, so an active-arm figure quoted without its comparator overstates the drug effect by roughly that much.
Concretely, duration decides what can be seen. A 68-week trial can measure weight and glycaemia; it cannot measure anything whose event rate is one per cent per year without enrolling tens of thousands.
Where a result is quoted from a conference abstract rather than a peer-reviewed publication, the numbers routinely move between the two. It is worth checking which one you are reading.
The short version: check the endpoint, check the comparator, check who was excluded, then look at the number.
edited 30 Oct 2025 by Dr_Rosalind_Achebe — added the placebo-arm figures
Analytical standards and reagents with traceable certificates. Every quantitative result you read inherits the accuracy of the standard behind it.
Shop standardsBefore comparing two trials, check whether they share an endpoint definition. Frequently they do not, and the numbers then are not comparable in any sense.
Trial populations are selected. Exclusion criteria in this class routinely remove people with significant renal impairment, prior pancreatitis and unstable psychiatric illness, which is exactly the population the results are then quoted for.
In practice, open-label extensions are not the same evidence as the randomised phase. Once everyone knows what they are taking, the reported outcomes acquire a bias that no analysis fully removes.
Quote the interval alongside the estimate and half the disagreements on this site would not start.
Answer first: read the primary endpoint, the comparator and the population before you read the effect size. Almost every argument on this site about a trial is really an argument about one of those three.
A composite endpoint is only as informative as its least serious component. Where a cardiovascular composite combines death, infarction and stroke, ask which component moved, because they are not interchangeable outcomes.
It helps to be literal here: non-inferiority and superiority designs are not interchangeable. A non-inferiority result says the new agent is not meaningfully worse against a pre-specified margin — it does not say it is as good, and it certainly does not say it is better.
Read the protocol and the statistical analysis plan if the result matters to you. Both are usually published alongside.
Ask PeptideStack is a static archive. Posting is closed, but the norms are worth stating: answer the question that was asked, show your working, cite the trial or the certificate, and say plainly where the evidence runs out.