This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
On post #29 — agreed on the reasoning, with one qualification.
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.
Picking up post #29: that is the part I would want checked first.
The claim about run-in periods upthread is stronger than its source supports. I have read the source. The source says "associated with" and the post says "causes".
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.
The documentation on run-in periods is better than this thread and I say that as someone who has posted in the thread.
My understanding of run-in periods is a few years old and may have been superseded. If it has been, I would genuinely like to know rather than keep repeating it.
Two sentences on run-in periods and then I will stop, because the rest is speculation and the thread is better without mine.
What is documented is narrow. What is inferred from it is broad. The gap between them is where every argument here lives.
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.
Second-hand on run-in periods, so weight it accordingly — someone whose method I trust told me this and I have not verified it myself.
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.
I read post #41 twice before replying, because I had assumed the opposite.
A methods point on run-in periods rather than a substantive one: if the comparison is not like for like, the difference you are measuring is the difference in method.
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 #46, because the general version has more than one answer.
I have no financial interest in anything named in this thread and I want to say so before I comment on run-in periods, because it is the sort of subject where it matters.
Adding the measurement that post #46 says would settle it.
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.
The interesting part of this is the exception, and I do not understand the exception.
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 #51 holds. The case it does not cover is the one I have.
I think the run-in periods question is answerable and has not been answered, which is a more optimistic position than most of this thread.
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.
On run-in periods, the part that usually goes wrong is that the question is asked as though it has one answer. It has a range, and the width of the range is the interesting bit.
If you can post the two or three numbers you are working from, several people here will check the arithmetic rather than argue about the conclusion.
The arithmetic in post #55 is right; the assumption feeding it is the part to check.
Run-in periods has been discussed here with more heat than it deserves, mostly because two definitions have been in play the whole time.
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.
This follows post #59 rather than contradicting it.
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.
That has been true for the cases I have seen and I have not seen many.