Reverse causation in a cohort study of weight and outcome — a second dataset Writing it up because I had to work it out twice and would rather nobody else did.
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.
Fine by me. I had wanted a stronger conclusion and there is not one available.
Posting my Reverse causation numbers with the method attached so they can be discounted properly. Uncontrolled, unblinded, and collected by someone who wanted a particular answer.
The arithmetic in the opening post is right; the assumption feeding it is the part to check.
Criticism is more useful when it is narrower. "The trial answers a different question from the one being asked" is actionable; "the trial is flawed" is not.
The opening post put the caveat in the right place and I want to underline it.
Hold a trial to the standard something could actually have met. A criticism that no achievable design could have answered is a criticism of the field rather than of the paper.
On Reverse causation the community has more anecdote than the confidence in this thread implies, and I include my own contribution in that.
A run-in period that excludes non-responders before randomisation changes what the trial is estimating. It is legitimate design and it must be stated in any summary.
The disagreement above is smaller than it looks once the terms are fixed.
Nothing to add on the substance. Thank you for taking the question at face value.
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.
Reverse causation would be much easier to settle if anyone reported the denominator. Almost nobody reports the denominator.
Post #12 put the caveat in the right place and I want to underline it.
Surrogate endpoints are not automatically bad and their validity is compound-specific and population-specific. The question is whether this surrogate has been validated for this use.
If that reads as pedantic, it is, and it has saved me twice.
Where the Reverse causation reasoning breaks down for me is the step from the group result to the individual case. That step is almost never argued for.
Post #14 is the version of this I will quote in future. One addition.
Generalisability and validity are separate axes. A trial can be internally impeccable and still tell you nothing about the person asking.
Small point, but it is the one that usually catches people.
Where I would push back on the Reverse causation consensus is the confidence, not the direction. The direction looks right. The confidence is borrowed.
Noted, and thank you for writing it out rather than summarising it.
This follows post #21 rather than contradicting it.
Attrition is the failure mode most likely to invalidate a result and the least likely to be discussed. Differential attrition between arms is the specific thing to look for.
I looked this up rather than remembered it, which is the right order.
That is the distinction I keep failing to hold on to. Written down now.
Picking up post #23: that is the part I would want checked first.
An honest declaration on Reverse causation: I have a prior here and it is strong enough that you should weight what I say downward. Stating it rather than hiding it.
I had written a reply contradicting post #26 and deleted it. Here is what survived.
Generalisability: do the inclusion/exclusion criteria narrow the population so much that results do not apply to real people asking about it? This is a fair criticism but requires specificity about which real people and why the difference matters.
The evidence for this is thinner than the way I have phrased it suggests.
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.
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.