Coming back to: Sample size calculations, read backwards from the published number

I read post #4 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.

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.

Adding the caveat now so it does not have to be extracted later.

Understood. Thank you for being specific about the limits of it.

Noted, and thank you for writing it out rather than summarising it.

An update on my earlier Sample size calculations post: the pattern held for another six weeks and then stopped, which I did not predict and cannot explain.

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.

I would be cautious about generalising from the Sample size calculations example above. It is a good example. It is one example.

Practical answer on Sample size calculations, 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 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.