Interim analyses and stopping rules Writing it up because I had to work it out twice and would rather nobody else did.
Asking about interim analyses and stopping rules on behalf of the question I keep seeing asked badly, including by me.
Framed properly it is answerable. Framed the usual way it is not, and that is most of why the previous threads went nowhere.
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.
My understanding of interim analyses and stopping rules is a few years old and may have been superseded. If it has been, I would genuinely like to know rather than keep repeating it.
Coming back to post #5, because the follow-up matters more than the original answer.
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.
Two people can read the same figure differently here and both be reasonable.
Picking up post #5: that is the part I would want checked first.
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.
Clear enough that I do not think I have a follow-up, which is unusual.
The reason interim analyses and stopping rules is hard to answer is that the obvious measurement and the relevant quantity are not the same thing, and substituting one for the other is silent.
Post #8 answers the question as asked. The question underneath it is different.
I would put moderate confidence on the mainstream reading of interim analyses and stopping rules and no more. That is not scepticism for its own sake; it is where the sourcing actually stops.
On post #8 — agreed on the reasoning, with one qualification.
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.
A single observation, in a thread that deserves better than single observations.
Answering the interim analyses and stopping rules question as asked, then the question I think is meant. As asked: yes, with the qualification below. As meant: it depends on how the first measurement was taken.
Helpful, and easy to find again, which is half of what a good reply is.
Post #13 is right about the mechanism and I think understates the practical bit.
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.
The answer changed when I changed how I was measuring, which was informative.
Reading back through the interim analyses and stopping rules threads from last year, the same three questions come up every time and only one of them has ever been answered properly. That seems like a documentation gap rather than a knowledge gap.
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.
That holds for the case as described. Change the assumptions and it may not.
Taking interim analyses and stopping rules seriously for a moment rather than deflecting: the honest position is that the community has observations and no controlled comparison, and those two things support very different sentences.
Interim analyses and stopping rules is worth one more sentence than it usually gets, and the sentence is the one about how the number was arrived at.
I read post #20 twice before replying, because I had assumed the opposite.
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.
The interesting part of this is the exception, and I do not understand the exception.
This follows post #23 rather than contradicting it.
Trying to state the interim analyses and stopping rules position in a way that someone who disagrees would recognise as fair, because I do not think the version in this thread passes that test.
An update on my earlier interim analyses and stopping rules post: the pattern held for another six weeks and then stopped, which I did not predict and cannot explain.
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.
That is a description of practice, not a recommendation of it.
Picking up post #26: that is the part I would want checked first.
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.
I have kept the units in throughout, for the obvious reason.
Worth separating two things that post #26 runs together.
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.
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