[2026 update] Composite endpoints and the component doing the work

On the subject in the title: Composite endpoints and the component doing the work Working notes rather than a conclusion.

Comparing SURMOUNT-1 (N Engl J Med, 2022) with SURPASS-4 (Lancet, 2021) and finding the comparison harder than it looks.

Different populations, different durations, different endpoints defined slightly differently, and in one case a different estimand. People compare the headline percentages anyway, including me until recently.

Is there a defensible way to put these side by side, or is the honest answer that there is not and we should stop?

Registration before enrolment, with the primary endpoint declared, is what makes outcome switching detectable. Checking the registry against the paper takes five minutes and is worth doing.

Post #2 answers the question as asked. The question underneath it is different.

A definition problem is doing most of the work in this Composite endpoints discussion. Once the term is pinned down I suspect the disagreement mostly goes away and what is left is small.

On the opening post — agreed on the reasoning, with one qualification.

What I would check first on Composite endpoints is whether the thing being measured moved or whether the way of measuring it moved. Those look identical in a graph.

Absolute and relative effects answer different questions. Write down the event rate in each arm and the difference between them; everything quotable is derived from those two numbers.

Post #5 is right about the mechanism and I think understates the practical bit.

Composite endpoints should be read component by component. A composite driven entirely by its softest component is a different finding from one where the components move together.

Old habit: I write down the expected answer before I calculate it.

Reading this Composite endpoints thread as someone who came in with a fixed view: the third and seventh replies moved me and the confident ones did not.

Composite endpoints has a well-known answer and a correct answer, and the interesting work is establishing that they are the same. Nobody has done that here yet.

Coming back to post #8, because the follow-up matters more than the original answer.

Absolute numbers, not just relative: a 30% relative reduction tells you the ratio but not the practical magnitude. The event rate in each arm and the difference between them tells you how many people benefit.

That is the practical version. The rigorous version is longer and says the same thing.

Adding the boring version of Composite endpoints, because the interesting version keeps getting posted and the boring one is usually right.

Check the ordinary explanations, in order, and stop when one of them accounts for what you are seeing. Most of the time the second one does.

Post #11 answers the question as asked. The question underneath it is different.

An open-label trial is not worthless and its subjective endpoints deserve more scepticism than its objective ones. That is a graded judgement rather than a verdict.

That is a description of practice, not a recommendation of it.

Coming back to post #11, because the follow-up matters more than the original answer.

Two things can be true about Composite endpoints at once: the mechanism is plausible and the evidence for the size of the effect is thin. Most of the argument here is people defending the first against attacks on the second.

I keep a log for Composite endpoints specifically because my memory of it turned out to be systematically wrong in one direction. Six weeks of notes cost nothing and settled it.