Run-in periods and the population they select posts 121–143
This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
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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.
A modest claim, modestly supported.
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.
Following, with nothing to contribute beyond having asked the same thing elsewhere.
I read post #121 twice before replying, because I had assumed the opposite.
One caution on run-in periods: everything above assumes the underlying documentation is what it claims to be. That assumption is doing real work and is rarely stated.
Narrowing post #125, because the general version has more than one answer.
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.
It is the sort of thing that seems obvious in retrospect and was not at the time.
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.
Run-in periods is a good example of a question where the honest answer is boring and the interesting answers are unsupported. I would go with boring.
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Grateful for the specificity. Vague answers to this question are what sent me looking.
Post #128 answers the question as asked. The question underneath it is different.
Adding a small correction to the run-in periods summary above rather than a disagreement with it. The substance holds; one of the figures is out by a factor that matters.
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.
Everything in post #130 holds. The case it does not cover is the one I have.
A note on scope: what I am saying about run-in periods applies to the case in the first post and I would not extend it further without checking.
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.
Adding this to the thread rather than to the wiki, because I am not confident enough for the wiki.
Noted, and thank you for writing it out rather than summarising it.
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.
What I would tell a new member reading about run-in periods for the first time: the confident posts are not the reliable ones, and the reliable ones are longer.
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.
Fair, and the limits you put on it are the part I will remember.
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On post #140 — agreed on the reasoning, with one qualification.
On run-in periods: the maintained page in the documentation commons covers the general case with citations and a review date, which is more reliable than any reply here including this one.
No notes. Posting so the count is not one.
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.
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