This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
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.
Answering the Adjudicated events 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.
Post #61 is the version of this I will quote in future. One addition.
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 question post #61 raises rather than the one it answers.
The reason Adjudicated events keeps being re-asked is that the answer is conditional and people quote it without the condition. It is not that the answer is unknown.
That is a cleaner way of putting what I was circling around.
Taking Adjudicated events 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.
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 one dataset and I would not build a rule on it.
Narrowing post #69, because the general version has more than one answer.
An observation about Adjudicated events that I cannot explain and am posting anyway, on the principle that unexplained observations are more useful public than private.
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.
Seconded. It reads as careful rather than confident, which is the right register.
Post #70 put the caveat in the right place and I want to underline 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.
One case, stated as one case.
Adjudicated events would be much easier to settle if anyone reported the denominator. Almost nobody reports the denominator.
I read post #74 twice before replying, because I had assumed the opposite.
Adding a null result on Adjudicated events. I looked, carefully, and found nothing, and null results deserve posting precisely because they never are.
Taking post #75 at face value and following it one step further.
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.
Take it as a starting point and not as a specification.
Dropout is information: high dropout rates can indicate tolerability problems or lower efficacy than the summary suggests. Where the analysis handled dropouts matters. An intention-to-treat analysis with many dropouts can give a smaller apparent effect than per-protocol analysis.
One more caveat and then I will stop qualifying: the sample selected itself.
That is clearer than the version I had in my head. Thank you.
Reframing Adjudicated events slightly, because I think the disagreement is about the question rather than the answer. If the question is "does it happen", yes. If it is "how often", nobody here knows.
Post #80 describes the usual case. This is about the unusual one.
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.
Not disagreeing with anyone above, just adding the bit I keep having to look up.
Adding the measurement that post #80 says would settle it.
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.
It is worth stating the boring hypothesis before the interesting one.
The honest answer on Adjudicated events is that it depends, and the useful part is the list of what it depends on. Four items, in rough order of how much they matter.
Most people get the first two right and then argue about the fourth.
Fair, and the limits you put on it are the part I will remember.
Reporting rather than recommending, on Adjudicated events. What happened is above. Whether it should have is a different question and not one I am qualified to answer.
Picking up post #83: that is the part I would want checked first.
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.
It is worth checking rather than assuming, which costs nothing.
Counterpoint on Adjudicated events, offered without confidence: the same observation is consistent with a much duller explanation, and nobody has ruled the dull one out.
The most useful reply I ever got about Adjudicated events was a request to state my units. It sounds like pedantry and it has saved me twice.