Confounding by indication, explained with a concrete example

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.

Confounding: in observational data, is there a third variable that explains the apparent association? In randomised data, randomisation should balance unknown confounders, though known confounders can be adjusted for.

If the premise is wrong, everything after it is decoration.

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 conclusion is tentative; the arithmetic underneath it is not.

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 step people skip is the one I have spelled out.

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.