How to read a forest plot, properly, from scratch

Picking up post #12: 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.

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.

Good question, well framed, and I would like to see it answered properly.

This follows post #16 rather than contradicting 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.

Adding a source would improve this post and I do not have one to hand.

Nothing to add, except that this is the answer I would give if asked.

Same experience here, different supplier, so it is at least not unique to one of them.

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.

Adding it because I spent an afternoon working it out and nobody should have to twice.

Worth separating two things that post #19 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.

Post #23 is right about the mechanism and I think understates the practical bit.

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.

Coming back to post #23, 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.

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 guess, clearly labelled as one.

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 hold that lightly until someone with a larger sample weighs in.

Adding a note of thanks rather than an opinion. I did not know most of that.

I read post #27 twice before replying, because I had assumed the opposite.

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.

It cost nothing to check and would have cost something not to.

This follows post #27 rather than contradicting it.

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.