Stated plainly: SURMOUNT-5 · reflux · 15 mg.
I would like help reading this properly rather than being told what conclusion to reach.
I have the full report including the method section, so I can quote specifics if that helps.
What can I legitimately conclude from this figure?
Only what the 15 mg arm reported, and the denominator is that arm rather than the trial. Adverse-event tables are published per arm, so the 15 mg incidence of reflux 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 — reflux 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 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.
The underlying point is that 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.
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 short version: check the endpoint, check the comparator, check who was excluded, then look at the number.
edited 21 Oct 2024 by gradient_slope — updated for the 2026 guidance change
Analytical standards and reagents with traceable certificates. Every quantitative result you read inherits the accuracy of the standard behind it.
Shop standardsLook 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.
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.
On the detail: 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.
Quote the interval alongside the estimate and half the disagreements on this site would not start.
edited 20 Nov 2024 by Dr_Rosalind_Achebe — tightened the wording; no substantive change
The relevant detail is that 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.
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.
Mechanically, 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.
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.
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.
I am not a clinician and this is not medical advice; it is a reading of a published protocol.
Read the protocol and the statistical analysis plan if the result matters to you. Both are usually published alongside.
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.