Measurement error in a self-reported exposure posts 61–90
This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1 · go to the accepted answer.
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 disagree with the reply above, and I think the disagreement is substantive rather than terminological.
The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.
On post #60 — agreed on the reasoning, with one qualification.
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.
This follows post #62 rather than contradicting it.
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.
Two things before anyone answers the substance.
First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.
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 Asking whether there are unpublished negative trials is a fair critical question.
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.
Picking up post #66: that is the part I would want checked first.
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.
Coming back to post #68, because the follow-up matters more than the original answer.
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.
post #71 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.
Coming back to post #71, because the follow-up matters more than the original answer.
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.
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 #71 runs together.
Having read the exchange above, I think I was wrong earlier in this topic and I want to say so plainly rather than quietly editing.
The correction was fair and I had been repeating something I had not checked carefully enough.
post #75 is right about the mechanism and I think understates the practical bit.
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.
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.
Collapsed as off-topic by two members at trust level 3 or above
On post #75 — agreed on the reasoning, with one qualification.
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.
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.
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.
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 #80: that is the part I would want checked first.
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.
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.
Collapsed as off-topic by two members at trust level 3 or above
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.
Worth separating two things that post #82 runs together.
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.
I disagree with the reply above, and I think the disagreement is substantive rather than terminological.
The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.
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.
post #88 answers the question as asked. The question underneath it is different.
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.
On post #86 — agreed on the reasoning, with one qualification.
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.