What does SURMOUNT-5 actually establish about tirzepatide?

What I have: SURMOUNT-5 · tirzepatide.

I can parse the result. I am less sure what it licenses me to conclude.

I have deliberately not looked at anyone else’s interpretation yet.

Which parts of this are informative and which are decoration?

Whatever its primary endpoint was, at the power it was designed for, in the population it recruited — and nothing else. SURMOUNT-5 was sized to answer one question. Every other result in it is a secondary or exploratory endpoint, powered incidentally if at all, and a nominally significant secondary in a programme with twenty of them is what you would expect from chance alone. So the reading order is: primary endpoint, then whether the secondaries were pre-specified and hierarchically tested, then everything else as hypothesis-generating. A trial establishes one thing well and suggests several things badly, and the press coverage inverts that ranking reliably.

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.

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.

Stated carefully, 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.

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.

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

More usefully, this is answerable from the published record, but only if you take the placebo arm seriously rather than reading the active arm alone.

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.

Concretely, 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.

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.

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.

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.

To be exact about it, 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.

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.

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.

edited 6 Jun 2024 by Dr_Rosalind_Achebe — corrected a unit error in the worked example

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.

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.

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

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

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

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.

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 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.

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