This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
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.
The interesting part of this is the exception, and I do not understand the exception.
Worth separating two things that post #30 runs together.
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.
Written from notes rather than memory, which is why the numbers are specific.
Surrogate endpoints: an endpoint that is not the outcome that matters but is measured as a stand-in. HbA1c is a surrogate for long-term glucose control and the short-term complications it prevents. Weight loss is a surrogate for metabolic health and long-term outcomes. Surrogates are useful but not identical to the endpoint that matters.
Small correction to my own earlier position on Adjudicated events. I had the units the wrong way round, which changes the conclusion by an order of magnitude and therefore changes it entirely.
Narrowing post #36, because the general version has more than one answer.
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.
It is the sort of thing that seems obvious in retrospect and was not at the time.
Thank you for the correction. I would rather find out here than later.
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.
Nobody has said the unglamorous part of Adjudicated events yet, so: most of the variation is explained by things that are boring to write about and easy to check.
Quietly grateful for the plain phrasing. Not every thread gets that.
Coming back to post #40, because the follow-up matters more than the original answer.
On Adjudicated events: the maintained page in the documentation commons covers the general case with citations and a review date, which is more reliable than any reply here including this one.
Adjudicated events is well covered in the tag pages, and the older discussions are better than the recent ones because they were argued out properly. Worth twenty minutes before adding to this one.
Adding the measurement that post #43 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.
Nothing above should be read as advice about what anyone else should do.
My experience of Adjudicated events contradicts the reply above. I am posting it as a data point rather than as a refutation, because one person's experience is exactly that.
Reading back through, this was answered upthread and I missed it. My fault.
This follows post #47 rather than contradicting it.
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.
Surrogate endpoints: an endpoint that is not the outcome that matters but is measured as a stand-in. HbA1c is a surrogate for long-term glucose control and the short-term complications it prevents. Weight loss is a surrogate for metabolic health and long-term outcomes. Surrogates are useful but not identical to the endpoint that matters.
This is where my knowledge stops and I would rather mark the edge than blur it.
Post #48 describes the usual case. This is about the unusual one.
Where the Adjudicated events discussion usually stalls is that nobody wants to say "I do not know" and everyone is willing to say "it varies". Those are the same sentence with different clothes on.
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 have seen it go both ways, which is why I hedge.
Offering a way to settle Adjudicated events rather than another opinion about it. Two measurements, taken the same way, a fortnight apart. If the difference is within the noise, the question was not answerable at this precision.
Picking up post #54: that is the part I would want checked first.
Second-hand on Adjudicated events, so weight it accordingly — someone whose method I trust told me this and I have not verified it myself.
On post #52 — 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.
I have three months of notes on Adjudicated events and the honest summary is that the trend is real and the week-to-week numbers are noise. I nearly drew the opposite conclusion from the first fortnight.
This follows post #57 rather than contradicting it.
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.
Scoping that to what I have actually seen rather than what I have read.
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.
Second-hand, so weight it accordingly.