Comparators chosen for regulatory reasons rather than clinical ones — one year on

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.

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.

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.

This follows post #34 rather than contradicting it.

Comparators chosen for regulatory reasons is well covered in the tag pages, and the older discussions are better than the recent ones because they were argued out properly. Worth twenty minutes before adding to this one.

Where the Comparators chosen for regulatory reasons discussion usually stalls is that nobody wants to say "I do not know" and everyone is willing to say "it varies". Those are the same sentence with different clothes on.

Building on post #49 rather than restating it.

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.

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.

If anyone has run this properly I would rather read that than my own guess.

This follows post #74 rather than contradicting 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.

Reading it again, the caveat matters more than the finding.