Reverse causation in a cohort study of weight and outcome

On the subject in the title: Reverse causation in a cohort study of weight and outcome Working notes rather than a conclusion.

Posting a small dataset on reverse causation. 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.

Reverse causation is worth one more sentence than it usually gets, and the sentence is the one about how the number was arrived at.

Adding the measurement that post #2 says would settle it.

Trying to state the reverse causation position in a way that someone who disagrees would recognise as fair, because I do not think the version in this thread passes that test.

Multiple comparisons: if a paper reports many outcomes, the chance of a spurious association by random chance is real. Pre-specification of primary outcomes matters and secondary analyses are weaker evidence.

That holds under the stated conditions and I have stated them.

Reverse causation looks different depending on whether you are reading the primary literature or the summaries of it, and the difference is not in our favour.

Small methodological point on reverse causation: repeating a measurement is cheap and resolves most of what is being argued about here at no cost to anyone.

The arithmetic in post #6 is right; the assumption feeding it is the part to check.

Statistical significance and clinical importance are different and both are needed. A significant difference below the minimal important difference is a real finding of no practical consequence.

I have changed my mind on this once already, so take it as current rather than settled.

Answering the question post #4 raises rather than the one it answers.

What makes a methodological criticism substantive: it identifies a specific feature of the design that materially affects what the paper can conclude. "Small sample size" alone is weak. "Small sample size for a rare outcome, so the confidence interval is wide" is stronger.

If it helps: the failure mode here is usually boring rather than dramatic.

Post #6 is right about the mechanism and I think understates the practical bit.

Reverse causation would be much easier to settle if anyone reported the denominator. Almost nobody reports the denominator.

Following, with nothing to contribute beyond having asked the same thing elsewhere.

What I would tell a new member reading about reverse causation for the first time: the confident posts are not the reliable ones, and the reliable ones are longer.

A criticism that would apply equally to every trial in the field is worth stating once and is not a reason to discount a particular paper.

Worth separating two things that post #11 runs together.

Distinguishing three things in the reverse causation discussion that keep getting used interchangeably: the observation, the proposed mechanism, and the recommendation that gets attached to both.

This follows post #11 rather than contradicting it.

An update on my earlier reverse causation post: the pattern held for another six weeks and then stopped, which I did not predict and cannot explain.