Immortal time bias in a claims-database study — one year on posts 31–47

When you change your mind: if a reply convinces you that your criticism was not well-founded, say so plainly. The critique might still be real but smaller than you originally thought. That is not a failure — it is how discussion works.

Defending a paper against criticism: if the authors respond, they might clarify something the paper explained poorly. Their response might also miss your point. Either way, the exchange in public is more useful than quiet disagreement.

I read post #31 twice before replying, because I had assumed the opposite.

Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.

This follows post #31 rather than contradicting it.

Choosing the worst interpretation: "The confidence interval includes a harmful effect" is true if the CI goes from -1 to +5. But assuming the worst-case scenario is not how you use the evidence. The point estimate and the precision both matter.

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.

For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.

Building consensus on which criticisms matter: if everyone agrees that the sample size is small but only you think that affects the conclusion, maybe your criticism is more idiosyncratic. That does not make it wrong but it is worth noticing.

Picking up post #35: that is the part I would want checked first.

Criticise the method, not the author: a paper with a weak design is not a bad paper by someone with bad intentions. It is a paper that answers a limited question. Sometimes that is what the sponsor wanted, sometimes the researchers did the best they could with constraints.

Worth separating two things that post #35 runs together.

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.

Publication bias: a single published positive trial is weaker evidence than multiple published trials with consistent results. Asking whether there are unpublished negative trials is a fair critical question.

post #40 is right about the mechanism and I think understates the practical bit.

Choosing the worst interpretation: "The confidence interval includes a harmful effect" is true if the CI goes from -1 to +5. But assuming the worst-case scenario is not how you use the evidence. The point estimate and the precision both matter.

Publication bias: a single published positive trial is weaker evidence than multiple published trials with consistent results. Asking whether there are unpublished negative trials is a fair critical question.

For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.

I read post #42 twice before replying, because I had assumed the opposite.

Bias towards the null and bias away from the null: different criticisms have different directions. Differential dropout might bias away from null; conservative statistical analysis might bias toward null.