What does SURMOUNT-5 tell me about dizziness at the 10 mg dose?

The case in front of me: SURMOUNT-5 · dizziness · 10 mg.

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 can I legitimately conclude from this figure?

Only what the 10 mg arm reported, and the denominator is that arm rather than the trial. Adverse-event tables are published per arm, so the 10 mg incidence of dizziness has its own numerator and its own denominator, and pooling it with the other arms produces a figure that describes nobody. Two further deductions before you use it. Subtract the placebo arm — dizziness occurs in people who received nothing, and the difference is the part attributable to the drug. And check whether SURMOUNT-5 counted events or counted participants: one participant with six episodes is one row in a participant count and six in an event count, and the two get quoted interchangeably. Nothing here is medical advice.

The trial answers a narrower question than the headline suggests, and the narrowing is where the useful information is.

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.

Headline results, principal programmes

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.

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.

I am not a clinician and this is not medical advice; it is a reading of a published protocol.

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

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.

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.

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.

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.

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

edited 21 Apr 2026 by ellis_thorne — expanded the table to cover the lower concentration

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.

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.

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.

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

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

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.

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.

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.

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

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.

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.

The caveat is the population. Trial participants were screened, monitored and supported; the effect size in an unmonitored setting is not the trial effect size, and it is not obvious in which direction the difference runs.

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