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. Marking that as an opinion rather than a finding.
On the subject in the title: Primary endpoint hierarchies and why order matters — one year on Working notes rather than a conclusion.
A follow-up question about Primary endpoint hierarchies that I did not know to ask the first time.
The earlier thread answered what I asked. What I should have asked is below, and I think it is the one that matters.
The arithmetic in post #2 is right; the assumption feeding it is the part to check.
Primary endpoint hierarchies came up in a thread eighteen months ago and was answered well. I cannot find it, which is itself the problem, so here is the reconstruction.
Answering the question the opening post raises rather than the one it answers.
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.
Taking post #2 at face value and following it one step further.
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.
Reporting rather than recommending, on Primary endpoint hierarchies. What happened is above. Whether it should have is a different question and not one I am qualified to 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.
I had written a reply contradicting post #8 and deleted it. Here is what survived.
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.
Primary endpoint hierarchies is worth one more sentence than it usually gets, and the sentence is the one about how the number was arrived at.
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.
I would rather post the uncertainty than round it away.
Adding the measurement that post #11 says would settle it.
Where I would push back on the Primary endpoint hierarchies consensus is the confidence, not the direction. The direction looks right. The confidence is borrowed.
Understood. Thank you for being specific about the limits of it.
The arithmetic in post #15 is right; the assumption feeding it is the part to check.
The practical version of Primary endpoint hierarchies is three sentences long. The rigorous version is three pages and reaches the same conclusion with the conditions attached.
On Primary endpoint hierarchies the community has more anecdote than the confidence in this thread implies, and I include my own contribution in that.
Building on post #19 rather than restating it.
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.
Post #20 is the version of this I will quote in future. One addition.
Primary endpoint hierarchies sits at the boundary between what this community can usefully discuss and what it cannot, and I think it falls on the discussable side, narrowly.
Where I part company with post #18, and it is a narrow parting.
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.
Someone will know this better than I do and I hope they say so.
Acknowledging rather than arguing. The reasoning holds as far as I can follow it.
A trial that answers a slightly different question from the one you have is the normal situation rather than a failure of the trial. The skill is describing the gap precisely.
That is my reading. Someone else read the same page differently and was reasonable.
What I would tell a new member reading about Primary endpoint hierarchies for the first time: the confident posts are not the reliable ones, and the reliable ones are longer.
I had written a reply contradicting post #22 and deleted it. Here is what survived.
Primary endpoint hierarchies would be much easier to settle if anyone reported the denominator. Almost nobody reports the denominator.
Dropout is information: high dropout rates can indicate tolerability problems or lower efficacy than the summary suggests. Where the analysis handled dropouts matters. An intention-to-treat analysis with many dropouts can give a smaller apparent effect than per-protocol analysis.
Surrogate endpoints: an endpoint that is not the outcome that matters but is measured as a stand-in. HbA1c is a surrogate for long-term glucose control and the short-term complications it prevents. Weight loss is a surrogate for metabolic health and long-term outcomes. Surrogates are useful but not identical to the endpoint that matters.
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.