Why do two papers on ATTAIN-1 report different headline figures?

This is a question about interpretation, not about whether to act — I will take action questions elsewhere.

I keep seeing this stated as a fact with no explanation attached, and unexplained facts make me suspicious.

My background is quantitative but not chemical, so I can follow an equation more easily than a hand-wave.

Why does this happen, and what would falsify the usual explanation?

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.

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.

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.

One qualification: absence of a signal in a trial of this size is not evidence of absence for a rare event. It is evidence that the event is rarer than the trial could detect.

Quote the interval alongside the estimate and half the disagreements on this site would not start.

edited 27 Nov 2025 by orla_ferriter — updated for the 2026 guidance change

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.

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.

Worth being precise here: 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.

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 caveat is that trial evidence is about licensed product administered under supervision. None of it transfers automatically to research-grade material of unverified content.

The short version: check the endpoint, check the comparator, check who was excluded, then look at the number.

edited 27 Nov 2025 by Dr_Tomas_Kral — tightened the wording; no substantive change

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.

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.

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.

Be careful about generalising from a trial population to yourself. The exclusion criteria are usually the most informative page in the supplement.

Read the protocol and the statistical analysis plan if the result matters to you. Both are usually published alongside.

Look at the discontinuation rate alongside the efficacy figure. A large effect in the two thirds who stayed is a different result from a large effect in everyone.

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.

If a claim cannot be traced to a named trial with a named endpoint, treat it as a claim rather than as evidence.