Sample size intuition for a personal experiment

On the subject in the title: Sample size intuition for a personal experiment Working notes rather than a conclusion.

Session topic: STEP 2 (Lancet, 2021). Please read it before posting; the discussion is much better when everyone has.

The question I would like us to start with is what the trial set out to estimate, rather than what it found. Once that is on the table we can talk about whether the design could have answered it, and only then about the numbers.

Specific things I would like covered: the population and how far it generalises, how discontinuation was handled, whether the comparator was a fair one, and what the absolute rather than relative effect looks like.

I will summarise at the end and the summary will feed the relevant digest page.

Absence of evidence and evidence of absence: if a study is small and finds no effect, that is absence of evidence, not evidence of absence. A larger study might find an effect that a small study missed.

post #2 answers the question as asked. The question underneath it is different.

Number needed to treat: how many people need to be treated to prevent one bad outcome or achieve one good outcome. More intuitive than relative risk reduction.

On the opening post — agreed on the reasoning, with one qualification.

Relative risk and odds ratios: both compare the rate in one group to the rate in another. Relative risk is easier to understand. Odds ratios are standard in many analyses but can be misinterpreted.

Power and sample size: a study might be too small to detect a real effect (low power). Sample size calculations help determine how many participants are needed to detect an effect of a given magnitude.