Run-in periods and the population they select posts 61–90
This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
On post #58 — agreed on the reasoning, with one qualification.
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.
That is where I would start, not where I would stop.
Filing a mild objection to the consensus on run-in periods. Mild because I might be wrong; an objection because nobody has addressed the case that does not fit.
Post #62 describes the usual case. This is about the unusual one.
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.
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.
That matches what I was told, which is not the same as knowing it.
Practical experience of run-in periods, offered as one case with the conditions stated, not as a general finding. Conditions first, because they are what make it interpretable.
The arithmetic in post #68 is right; the assumption feeding it is the part to check.
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.
A definition problem is doing most of the work in this run-in periods discussion. Once the term is pinned down I suspect the disagreement mostly goes away and what is left is small.
Second this, and I would have said it less carefully.
Building on post #69 rather than restating it.
The number people quote for run-in periods is a central estimate presented without its interval, and the interval is wide enough that the estimate is nearly uninformative on its own.
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.
Where I part company with post #73, and it is a narrow parting.
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.
That much is documented. The rest is how I have interpreted it.
The version of run-in periods that I was taught turned out to be a teaching simplification. Useful, and not true in the way I had assumed it was.
Collapsed as off-topic by two members at trust level 3 or above
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.
It is the kind of thing that is obvious once and never again.
Adding the measurement that post #76 says would settle it.
I have been on both sides of the run-in periods argument in this category within eighteen months, which should tell you how strong the evidence for either side is.
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 am aware this is the third time this month I have made this point.
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.
The arithmetic in post #82 is right; the assumption feeding it is the part to check.
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.
Reading this run-in periods thread as someone who came in with a fixed view: the third and seventh replies moved me and the confident ones did 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.
Everything in post #84 holds. The case it does not cover is the one I have.
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.
On run-in periods, I would rather understate and be corrected upward than overstate and be quoted. That is a house style here and it is a good one.