What did the placebo arm of SELECT report for headache?

What I have: SELECT · headache.

I want to understand what this actually establishes, as opposed to what it is being used to imply.

My concern is that I am being invited to draw a conclusion the data does not support.

What does this actually establish, and what does it not?

A trial establishes what happened to a defined group under a defined protocol. Extending it beyond that group is inference, and inference is allowed as long as it is labelled.

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.

Relative to absolute, worked

The last two rows describe the same finding. Only one of them is used in headlines.

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.

The cardiovascular outcome programme in this class runs to several large randomised trials — LEADER for liraglutide, SUSTAIN-6 and SELECT for semaglutide, REWIND for dulaglutide — and they are the reason the class is discussed as more than a weight intervention.

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

Start with what the trial was powered for. Everything else in the publication is secondary, exploratory, or a subgroup, and those three words mean three different things.

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.

It helps to be literal here: 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.

Registry entries at ClinicalTrials.gov carry the pre-specified primary endpoint with a timestamp, which is the cheapest available check on whether an endpoint was changed after the data were seen.

When two sources disagree, the answer is almost always in the methods section of the one you have not read.

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.

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.

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.

Meta-analyses in this area are dominated by whichever trial contributed the most participants, so read the forest plot rather than the summary estimate.

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

edited 8 Feb 2026 by Dr_Colm_Fitzhenry — clarified the distinction between purity and content

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.

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.

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 8 Feb 2026 by w_okoye — added the citation requested in comments

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.

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.

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

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

edited 23 Dec 2025 by bufferline42 — corrected a unit error in the worked example