Concretely: ATTAIN-1 · vomiting.
I would like to know the limits of what can be inferred from this.
What I am trying to avoid is over-reading a single result, which I have done before.
What would I need in addition before this supported a decision?
The ATTAIN-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.
Before comparing two trials, check whether they share an endpoint definition. Frequently they do not, and the numbers then are not comparable in any sense.
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.
Specifically, 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.
When two sources disagree, the answer is almost always in the methods section of the one you have not read.
edited 13 May 2025 by plate_count_9k — added a caveat about sampling
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Browse resultsAnswer 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.
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.
Intention-to-treat and per-protocol analyses answer different questions. ITT asks what happens if you offer the treatment; per-protocol asks what happens if it is taken as directed. The gap between the two is a measure of how tolerable the protocol was.
Read the protocol and the statistical analysis plan if the result matters to you. Both are usually published alongside.
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.
Put another way, 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.
Quote the interval alongside the estimate and half the disagreements on this site would not start.
The short version: the effect is real, the magnitude depends on the population, and the population is usually the part that gets dropped when a result is quoted second-hand.
Confidence intervals matter more than point estimates when two trials disagree. Two studies reporting fifteen and twenty per cent whose intervals overlap heavily have not disagreed about anything.
If a claim cannot be traced to a named trial with a named endpoint, treat it as a claim rather than as evidence.
The trial answers a narrower question than the headline suggests, and the narrowing is where the useful information is.
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.
Meta-analyses in this area are dominated by whichever trial contributed the most participants, so read the forest plot rather than the summary estimate.
The short version: check the endpoint, check the comparator, check who was excluded, then look at the number.
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.