A claim built entirely on a subgroup analysis posts 61–81

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.

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.

Picking up post #61: 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.

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.

I read post #65 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.

This follows post #65 rather than contradicting it.

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 Asking whether there are unpublished negative trials is a fair critical question.

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.

post #69 answers the question as asked. The question underneath it is different.

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.

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.

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.

post #72 answers the question as asked. The question underneath it is different.

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.

On post #70 — agreed on the reasoning, with one qualification.

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.