Interim analyses and stopping rules — does this still hold?

Interim analyses and stopping rules — does this still hold? — that is the question, and I have not found it answered plainly anywhere I have looked.

Comparing SCALE (N Engl J Med, 2015) with SURMOUNT-4 (JAMA, 2024) 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?

post #4 answers the question as asked. The question underneath it is different.

The estimand: what the trial set out to estimate. Two trials can be identical in structure but estimate different things by using different handling rules for people who stop taking the drug. Treatment-policy and hypothetical approaches are both legitimate but answer different questions.

Coming back to post #13, because the follow-up matters more than the original answer.

Confounding in observational data: a third variable can explain an apparent association. In a randomised trial, randomisation balances unknown confounders. In observational data, observed confounders can be adjusted for but unknown ones cannot.

On post #24 — agreed on the reasoning, with one qualification.

Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.