On the subject in the title: Reading a supplementary appendix and finding the interesting part Working notes rather than a conclusion.
Collecting what is known about Reading a supplementary appendix in one place, because it is currently spread across a category, two tag pages and a thread that is hard to find.
This is a summary rather than new work, and I have attributed each part to where I found 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.
The opening post and I disagree about the size of the effect, not about the direction.
Composite endpoints should be read component by component. A composite driven entirely by its softest component is a different finding from one where the components move together.
Post #2 is right about the mechanism and I think understates the practical bit.
Marking my uncertainty on Reading a supplementary appendix explicitly. I am confident about the direction, much less confident about the size, and not confident at all that it generalises past the case in the first post.
If someone has run Reading a supplementary appendix properly I would rather read that than my own reconstruction of it. Posting mine only because the thread has gone quiet.
Worth separating two things that post #4 runs together.
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.
Anyone with a larger sample, please post it.
Answering the question post #8 raises rather than the one it answers.
One more thing on Reading a supplementary appendix 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.
Worth separating two things that post #8 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.
Agreed on Reading a supplementary appendix, with one qualification that I think matters. The reasoning holds for the case as described. Change the starting assumption and it does not, and the starting assumption is the part nobody states.
Reading the supplementary appendix is where most of the real information is, and it is where almost nobody goes. The baseline table alone answers half the generalisability questions asked here.
Taking Reading a supplementary appendix 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.
That matches what I have seen, for whatever a single anecdote is worth.
Reading a supplementary appendix 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.
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.
Confirming post #16 from a second method, which matters more than confirming it from a second person.
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.
Reading it again, the caveat matters more than the finding.
My experience of Reading a supplementary appendix 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.
Nobody has said the unglamorous part of Reading a supplementary appendix yet, so: most of the variation is explained by things that are boring to write about and easy to check.
Adding the measurement that post #20 says would settle 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.
The version of Reading a supplementary appendix that circulates here is a simplification of a simplification. It is not wrong, but it has lost the conditions under which it holds, and those conditions are where the interesting cases live.
Discontinuation handling is the methodological detail that most changes a result and gets the least attention. Read how missing data was imputed before reading the effect size.
The number is defensible. The precision I gave it is not.
Where I part company with post #22, and it is a narrow parting.
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.
Flagging that the sources on this are thinner than the confidence in the thread suggests.
Right, and stated more narrowly than I would have dared to state it.
On post #22 — agreed on the reasoning, with one qualification.
Adding a reference point for Reading a supplementary appendix. Mine is a single case, collected without controls, and I am posting the method alongside it so it can be discounted appropriately.
Practical answer on Reading a supplementary appendix, since the theoretical one is upthread: do the simplest check first, write down the result, and only then decide whether the complicated explanation is needed. It usually is not.
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 am confident about the direction and much less about the magnitude.
Summarising the Reading a supplementary appendix thread so far, since it is long and the answer is buried: the first reply has the method, the fourth has the correction to it, and the rest is people agreeing at length.
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.
Filing this under things that are true until someone shows me otherwise.