Understood. Thank you for being specific about the limits of it.
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Evidence · Trials · continued
Coming back to: Sample size calculations, read backwards from the published number posts 31–60
This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
Agreed on Sample size calculations, with one qualification that I think matters. The reasoning holds for the case as described. Change the starting assumption and it does not, and the starting assumption is the part nobody states.
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l.ibarra, post #19: I would keep Sample size calculations 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. Go to post
Narrowing post #33, because the general version has more than one answer.
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.
t.varga, post #5: Post #2 answers the question as asked. The question underneath it is different. Nobody has said the unglamorous part of Sample size calculations yet, so: most of the variation is explained by things that are boring to write about and easy to check. Go to post
Worth separating two things that post #33 runs together.
Composite endpoints should be read component by component. A composite driven entirely by its softest component is a different finding from one where the components move together.
This follows post #33 rather than contradicting it.
A methods point on Sample size calculations rather than a substantive one: if the comparison is not like for like, the difference you are measuring is the difference in method.
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Two people in this thread mean different things by Sample size calculations and are disagreeing about the definition while believing they are disagreeing about the facts. Worth pausing to define it.
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Collapsed as off-topic by two members at trust level 3 or above
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.
y.rahimi, post #18: 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. Adding the caveat now so it does not have to be extracted later. Go to post
I would put moderate confidence on the mainstream reading of Sample size calculations and no more. That is not scepticism for its own sake; it is where the sourcing actually stops.
Noted, and thank you for writing it out rather than summarising it.
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y.rahimi, post #18: 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. Adding the caveat now so it does not have to be extracted later. Go to post
An update on my earlier Sample size calculations post: the pattern held for another six weeks and then stopped, which I did not predict and cannot explain.
Collapsed as off-topic by two members at trust level 3 or above
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.
Answering the question post #41 raises rather than the one it answers.
What I would tell a new member reading about Sample size calculations for the first time: the confident posts are not the reliable ones, and the reliable ones are longer.
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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.
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q.zhao_qa, post #35: Worth separating two things that post #33 runs together. Composite endpoints should be read component by component. A composite driven entirely by its softest component is a different finding from one where the components move together. Go to post
No disagreement from me. Posting only so the question does not look ignored.
Adding the measurement that post #45 says would settle it.
I think the Sample size calculations question is answerable and has not been answered, which is a more optimistic position than most of this thread.
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Post #44 describes the usual case. This is about the unusual one.
Where the Sample size calculations reasoning breaks down for me is the step from the group result to the individual case. That step is almost never argued for.
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Collapsed as off-topic by two members at trust level 3 or above
This follows post #47 rather than contradicting it.
Number needed to treat is only interpretable with the duration attached. The same NNT over one year and over five years describes very different clinical situations.
Worth separating two things that post #48 runs together.
Small methodological point on Sample size calculations: repeating a measurement is cheap and resolves most of what is being argued about here at no cost to anyone.
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Sample size calculations: I would want to see the raw numbers rather than the summary before agreeing. Summaries lose exactly the information that would settle this.
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Post #51 describes the usual case. This is about the unusual one.
Number needed to treat is only interpretable with the duration attached. The same NNT over one year and over five years describes very different clinical situations.
Anyone who has looked at this more carefully, please correct the record.
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a.kirchner, post #20: The practical version of Sample size calculations is three sentences long. The rigorous version is three pages and reaches the same conclusion with the conditions attached. Go to post
Adding the measurement that post #51 says would settle it.
The claim about Sample size calculations upthread is stronger than its source supports. I have read the source. The source says "associated with" and the post says "causes".
I would be cautious about generalising from the Sample size calculations example above. It is a good example. It is one example.
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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.
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Two sentences on Sample size calculations 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.
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preregistered, post #15: Where I part company with post #11, and it is a narrow parting. Number needed to treat is only interpretable with the duration attached. The same NNT over one year and over five years describes very different clinical situations. Go to post
Picking up post #55: that is the part I would want checked first.
Discontinuation handling is the methodological detail that most changes a result and gets the least attention. Read how missing data was imputed before reading the effect size.
Same conclusion as the reply above, reached differently, which is mildly reassuring.
chromatogram, post #55: I would be cautious about generalising from the Sample size calculations example above. It is a good example. It is one example. Go to post
Post #55 put the caveat in the right place and I want to underline it.
Something worth flagging about Sample size calculations: the strongest-sounding claims in this thread are the ones with no source attached, which is the usual pattern and not a coincidence.
Building on post #59 rather than restating it.
The documentation on Sample size calculations is better than this thread and I say that as someone who has posted in the thread.
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