On the subject in the title: Run-in periods and the population they select Working notes rather than a conclusion.
An honest uncertainty about run-in periods rather than a disguised assertion.
I do not know the answer and I have not been able to find one. What I have is the shape of the question, which may be worth more than my guess at the 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.
Posted with less confidence than the sentence structure implies.
Everything in the opening post holds. The case it does not cover is the one I have.
Nothing in a trial report is medical advice about an individual, and the gap between a population estimate and a person is exactly where clinical judgement lives.
This follows post #2 rather than contradicting it.
Run-in periods has a well-known answer and a correct answer, and the interesting work is establishing that they are the same. Nobody has done that here yet.
Adding a data point of agreement rather than a data point.
I read post #4 twice before replying, because I had assumed the opposite.
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 want to see it done twice before believing it once.
Picking up post #6: that is the part I would want checked first.
Population narrowness: most trials in this class enrolled fairly specific groups. Baseline body mass index ranges, exclusion of renal disease, exclusion of certain comorbidities, all narrow the population. Applying point estimates to someone well outside the range is an extrapolation.
That is the practical version. The rigorous version is longer and says the same thing.
I read post #12 twice before replying, because I had assumed the opposite.
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.
Post #12 answers the question as asked. The question underneath it is different.
For anyone finding this later: the short answer on run-in periods is that it depends on one thing, and the rest of the thread is people identifying which thing.
Run-in periods is one of those subjects where the general answer and the answer for a specific case diverge, and the thread will go in circles until someone says which one is being asked for.
This follows post #15 rather than contradicting it.
Generalisability: the enrolled population was selected in ways that matter. Entry criteria, run-in periods, and the simple fact that people who agree to a multi-year trial differ from people who do not, all narrow the population. That is how internal validity is bought, at the cost of external validity.
Coming back to post #15, because the follow-up matters more than the original answer.
I disagree with the framing of run-in periods above, and I think it is a substantive disagreement rather than a terminological one. Setting out why, so it can be checked.
The reasoning depends on an assumption that is doing a lot of work and is never stated. If the assumption holds, the conclusion follows. I do not think it holds generally.
Source for the run-in periods figure, since it was asked for. It is in the discussion rather than the abstract, which is why the version circulating is stronger than the paper is.
Reading the surrounding paragraph is worth the two minutes. The authors are more careful than their summarisers.
Where I part company with post #15, and it is a narrow parting.
Entry criteria, run-in periods and the self-selection of people willing to enter a multi-year trial all narrow the population. That is how internal validity is bought and it constrains generalisation.
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.
Effect sizes in a trial population reflect adherence achieved under trial conditions, which is generally better than adherence outside them.
If the premise is wrong, everything after it is decoration.
Useful. I had the fact and not the reason, which turns out to be the important half.
On post #22 — agreed on the reasoning, with one qualification.
Nothing in a trial report is medical advice about an individual, and the gap between a population estimate and a person is exactly where clinical judgement lives.
I would rather post the uncertainty than round it away.
An update on my earlier run-in periods post: the pattern held for another six weeks and then stopped, which I did not predict and cannot explain.
Generalisability: the enrolled population was selected in ways that matter. Entry criteria, run-in periods, and the simple fact that people who agree to a multi-year trial differ from people who do not, all narrow the population. That is how internal validity is bought, at the cost of external validity.
Not a conclusion. A place to stand while looking for one.
Everything in post #26 holds. The case it does not cover is the one I have.
I would keep run-in periods and the decision it usually gets used for separate in this thread. They are related and they are not the same question, and merging them is why the last one went badly.