Immortal time bias in a claims-database study — one year on

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.

Picking up post #13: that is the part I would want checked first.

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

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 Asking whether there are unpublished negative trials is a fair critical question.

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.

I read post #17 twice before replying, because I had assumed the opposite.

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.

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.

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.

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.

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.

Worth separating two things that post #20 runs together.

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.

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

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.

post #26 answers the question as asked. The question underneath it is different.

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.

On post #24 — agreed on the reasoning, with one qualification.

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.

I read post #28 twice before replying, because I had assumed the opposite.

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.