Adjudicated events and why the definition matters — the long version Writing it up because I had to work it out twice and would rather nobody else did.
A narrow question about Adjudicated events, deliberately narrow, because the broad version has been asked here four times and produced four long threads and no answer.
One question, stated units, stated method, and what I have already ruled out.
The opening post is the version of this I will quote in future. One addition.
Adjudicated events: I have looked for the primary source twice and failed twice. Either it does not exist or it is somewhere I do not know to look, and I would like to know which.
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.
I would put this at better than even and not much better.
Two questions I would want answered before drawing anything from the Adjudicated events data above: how were the cases selected, and what happened to the ones that dropped out.
Confirming post #4 from a second method, which matters more than confirming it from a second person.
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.
Adding this to the thread rather than to the wiki, because I am not confident enough for the wiki.
Posting my Adjudicated events numbers with the method attached so they can be discounted properly. Uncontrolled, unblinded, and collected by someone who wanted a particular answer.
The figure that circulates in coverage is almost always whichever estimand gives the larger effect. That is not fraud; it is selection, and it is why the paper matters more than the summary.
Coming back to post #6, 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.
This is the sort of thing that ought to be settled and apparently is not.
Where I would push back on the Adjudicated events consensus is the confidence, not the direction. The direction looks right. The confidence is borrowed.
An update on my earlier Adjudicated events post: the pattern held for another six weeks and then stopped, which I did not predict and cannot explain.
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 measurement that post #13 says would settle it.
An honest declaration on Adjudicated events: I have a prior here and it is strong enough that you should weight what I say downward. Stating it rather than hiding it.
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.
Old habit: I write down the expected answer before I calculate 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.
This follows post #18 rather than contradicting it.
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.
That is the practical version. The rigorous version is longer and says the same thing.
Worth separating two things that post #16 runs together.
Small methodological point on Adjudicated events: repeating a measurement is cheap and resolves most of what is being argued about here at no cost to anyone.
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.
If anyone has run this properly I would rather read that than my own guess.
Everything in post #21 holds. The case it does not cover is the one I have.
Having read the whole Adjudicated events thread before replying: the question in the first post has not actually been answered yet, and three of us have answered a nearby one instead.
Narrowing post #21, because the general version has more than one answer.
Adjudicated events looks different depending on whether you are reading the primary literature or the summaries of it, and the difference is not in our favour.
Answering the question post #25 raises rather than the one it answers.
Multiplicity and multiple comparisons: if a trial tests many hypotheses, the chance of a false positive on at least one by random chance increases. This is why pre-specification of the primary endpoint matters and why secondary endpoints are weaker evidence.
That is the shape of it. The detail is where I would expect to be corrected.
The practical version of Adjudicated events is three sentences long. The rigorous version is three pages and reaches the same conclusion with the conditions attached.
That is consistent with mine, for whatever one more account is worth.
Trying to state the Adjudicated events 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.