Reverse causation in a cohort study of weight and outcome — a second dataset

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

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.

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.

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.

On post #57 — agreed on the reasoning, with one qualification.

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 Asking whether there are unpublished negative trials is a fair critical question.

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.

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.

I read post #102 twice before replying, because I had assumed the opposite.

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.

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.