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 one dataset and I would not build a rule on it.
Comparators chosen for regulatory reasons rather than clinical ones — one year on — setting out what I have, and where I think it stops being reliable.
Posting a small dataset on Comparators chosen for regulatory reasons. It is mine, it is uncontrolled, and the method is stated so it can be discounted appropriately.
What I would like is not agreement but a second dataset collected by someone with no stake in mine. If one exists I would rather read it than argue for this one.
Open-label design: unblinded trials admit expectation effects. For weight-loss trials where one arm loses substantial weight and the other does not, complete blinding is impossible anyway. The unblinded nature is a limitation worth noting.
That is what the documentation says. What happens in practice is usually close.
Worth stating the null on Comparators chosen for regulatory reasons before we explain it: the observation may be nothing. That possibility deserves a sentence and usually does not get one.
A note on how Comparators chosen for regulatory reasons gets discussed rather than on Comparators chosen for regulatory reasons itself: the confident posts get the replies and the careful ones get ignored, and the careful ones have been right more often.
Pre-specification is the property that makes a primary endpoint trustworthy. An endpoint chosen after seeing the data can be the best endpoint in the world and it no longer carries the same guarantee.
A single observation, in a thread that deserves better than single observations.
I read the earlier replies on Comparators chosen for regulatory reasons twice before writing this, because I had assumed the opposite and wanted to be sure I was disagreeing with what was said rather than what I expected.
The arithmetic in post #6 is right; the assumption feeding it is the part to check.
Checked the Comparators chosen for regulatory reasons claim against the primary source this morning. It survives, with a narrower scope than the version quoted here. Posting the narrower scope.
On Comparators chosen for regulatory reasons I would separate what is worth knowing from what is worth acting on. The first list is long and the second is short, and conflating them is how threads get heated.
The number people quote for Comparators chosen for regulatory reasons is a central estimate presented without its interval, and the interval is wide enough that the estimate is nearly uninformative on its own.
Narrowing post #12, because the general version has more than one answer.
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.
Right, and stated more narrowly than I would have dared to state it.
One caution on Comparators chosen for regulatory reasons: everything above assumes the underlying documentation is what it claims to be. That assumption is doing real work and is rarely stated.
Post #16 answers the question as asked. The question underneath it is different.
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.
I read post #14 twice before replying, because I had assumed the opposite.
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.
Post #16 is the version of this I will quote in future. One addition.
The reason Comparators chosen for regulatory reasons is hard to answer is that the obvious measurement and the relevant quantity are not the same thing, and substituting one for the other is silent.
Two sentences on Comparators chosen for regulatory reasons 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.
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 note on scope: what I am saying about Comparators chosen for regulatory reasons applies to the case in the first post and I would not extend it further without checking.
Everything in post #19 holds. The case it does not cover is the one I have.
Confounding in observational data: a third variable can explain an apparent association. In a randomised trial, randomisation balances unknown confounders. In observational data, observed confounders can be adjusted for but unknown ones cannot.
Multiplicity and multiple comparisons: if a trial tests many hypotheses, the chance of a false positive on at least one by random chance increases. This is why pre-specification of the primary endpoint matters and why secondary endpoints are weaker evidence.
Adding a null result on Comparators chosen for regulatory reasons. I looked, carefully, and found nothing, and null results deserve posting precisely because they never are.
Adding a note of thanks rather than an opinion. I did not know most of that.
On post #27 — agreed on the reasoning, with one qualification.
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.
I have no interest in any supplier named above.
Picking up post #27: that is the part I would want checked first.
The useful distinction on Comparators chosen for regulatory reasons is between what was measured and what was inferred from it. Both end up in the same sentence and only one of them has error bars.