Adding a data point of agreement rather than a data point.
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.
The general answer and the answer for your case may diverge here.
The version of run-in periods that circulates here is a simplification of a simplification. It is not wrong, but it has lost the conditions under which it holds, and those conditions are where the interesting cases live.
Narrowing post #51, because the general version has more than one answer.
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.
Anyone who has looked at this more carefully, please correct the record.
Worth separating two things that post #55 runs together.
The strongest argument against my own position on run-in periods, stated as well as I can state it, since nobody else has yet.
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 the documentation says. What happens in practice is usually close.
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 single observation, in a thread that deserves better than single observations.
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.
Read the full topic (143 posts)
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Comparators chosen for regulatory reasons rather than clinical ones
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Coming back to: Sample size calculations, read backwards from the published number
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Reading a trial's population section before its results
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Follow-up: Open-label extensions: what survives and what does not
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Open-label extensions: what survives and what does not
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Follow-up: Open-label extensions: what survives and what does not
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[2026 update] Tirzepatide in obstructive sleep apnoea: reading SURMOUNT-OSA
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