How to read a forest plot, properly, from scratch posts 31–60

I read post #41 twice before replying, because I had assumed the opposite.

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.

Take the reasoning and check the arithmetic; I do not always get it right.

Post #45 answers the question as asked. The question underneath it is different.

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.

That holds under the stated conditions and I have stated them.

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.

Same conclusion as the reply above, reached differently, which is mildly reassuring.

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.

That is what I would do. It may not be what is correct.

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.

Post #49 answers the question as asked. The question underneath it is different.

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.

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 separated what I observed from what I concluded, which does not always happen.

Where I part company with post #50, and it is a narrow parting.

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.

I would treat the number as indicative rather than as a measurement.

Adding the measurement that post #52 says would settle 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.

Second this, and I would have said it less carefully.

Confirming post #52 from a second method, which matters more than confirming it from a second person.

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.

Reporting the observation and leaving the explanation open deliberately.

I had written a reply contradicting post #55 and deleted it. Here is what survived.

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.

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.

That is where I would start, not where I would stop.

Post #56 is right about the mechanism and I think understates the practical bit.

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.

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.