Adding the measurement that the opening post says would settle it. 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.
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.
Interim analyses and stopping rules, from the point of view of someone who has read the documentation and is still stuck.
Saying which parts I have understood so that nobody wastes time re-explaining them, and where exactly the understanding stops.
I had written a reply contradicting the opening post and deleted it. Here is what survived.
Where I would push back on the Interim analyses and stopping rules consensus is the confidence, not the direction. The direction looks right. The confidence is borrowed.
Adding the measurement that the opening post says would settle it.
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.
Interim analyses and stopping rules looks different depending on whether you are reading the primary literature or the summaries of it, and the difference is not in our favour.
Post #4 is the version of this I will quote in future. One addition.
On Interim analyses and stopping rules the community has more anecdote than the confidence in this thread implies, and I include my own contribution in that.
Grateful for the specificity. Vague answers to this question are what sent me looking.
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.
Two people can read the same figure differently here and both be reasonable.
Risk of bias: structured appraisal of internal validity. Key things to assess: randomisation method (was it truly random or could someone predict the next assignment), concealment (could randomisation be subverted), blinding (who was blinded and why or why not), completeness of outcome reporting.
Noting that the question and the thing people usually mean by it are different.
Checked the Interim analyses and stopping rules claim against the primary source this morning. It survives, with a narrower scope than the version quoted here. Posting the narrower scope.
Coming back to post #9, because the follow-up matters more than the original answer.
A treatment-policy estimand asks what happens to people assigned to a strategy, including those who abandon it. A hypothetical estimand asks what would have happened had everyone continued. Both are legitimate and they give different numbers.
Post #9 is right about the mechanism and I think understates the practical bit.
What I can speak to on Interim analyses and stopping rules is narrow, so I will keep it narrow rather than generalising from it. Beyond that boundary I do not know.
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.
I am aware this is the third time this month I have made this point.
The most useful reply I ever got about Interim analyses and stopping rules was a request to state my units. It sounds like pedantry and it has saved me twice.
Second this, and I would have said it less carefully.
Interim analyses and stopping rules is a question about a distribution, not about a value, and treating it as a value is what produces the confident wrong answers.
Risk of bias: structured appraisal of internal validity. Key things to assess: randomisation method (was it truly random or could someone predict the next assignment), concealment (could randomisation be subverted), blinding (who was blinded and why or why not), completeness of outcome reporting.
Adding the caveat now so it does not have to be extracted later.
Picking up post #17: that is the part I would want checked first.
One more thing on Interim analyses and stopping rules that took me far too long to see: the two figures people quote are not measuring the same quantity. Once you notice that, the apparent contradiction disappears.
Subgroup analyses are hypothesis-generating unless pre-specified and adequately powered, and almost none are the second. The interaction test matters more than the subgroup point estimate.
The variance between people here is larger than the effect being discussed.
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.
Speaking for myself and not for anyone else who has posted here.
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.
A note on scope: what I am saying about Interim analyses and stopping rules applies to the case in the first post and I would not extend it further without checking.
Answering the question post #20 raises rather than the one it answers.
Interim analyses and stopping rules has been discussed here with more heat than it deserves, mostly because two definitions have been in play the whole time.
Risk of bias: structured appraisal of internal validity. Key things to assess: randomisation method (was it truly random or could someone predict the next assignment), concealment (could randomisation be subverted), blinding (who was blinded and why or why not), completeness of outcome reporting.
This is the sort of thing the wiki should carry and currently does not.
What I would tell a new member reading about Interim analyses and stopping rules for the first time: the confident posts are not the reliable ones, and the reliable ones are longer.
Narrowing post #26, because the general version has more than one answer.
Interim analyses and stopping rules would be much easier to settle if anyone reported the denominator. Almost nobody reports the denominator.
Everything in post #24 holds. The case it does not cover is the one I have.
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.
Worth separating two things that post #28 runs together.
Interim analyses and stopping rules sits at the boundary between what this community can usefully discuss and what it cannot, and I think it falls on the discussable side, narrowly.