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by BenoitEssiambre
1345 days ago
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>could the observed effect be significant with a larger trial? Sure. But that's always true for a negative result. Sure, this is true, it's one of the reasons why results being significant or not is not very relevant. At some point you want to move towards whether the effect size is in a clinically relevant range or not. >The objection carries no information. Inasmuch as something like a confidence interval provides an idea of the range of the effect size, more data does carry more information. I know it's complicated to do this analysis properly with prediction intervals and such, but you have no choice if you want to be able to make good decisions with your data. A wide range estimate that doesn't allow you to make good clinical decisions is not useful. For clinical purposes, I would even have been more confortable treating an significant but small effect in support of the "let's not test" scenario, than this wide range where the effect could be large and positive or negative on the other side and we just don't know. Significant doesn't automatically mean "do the test" and vice versa. Effect size matters! A non-significant result because of a wide interval just doesn't tell you much useful information. |
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No. Significance is the only thing that matters here. If you don't have a significant result, you don't have a result. Making up stories about how the results coulda-woulda-shoulda been significant if only the study was different somehow is fine for bedtime or planning the next study, but absolutely irrelevant to interpreting the clinical trial in front of you.
The CI here is not actually that wide; I was being colloquial. It's an 80,000 person trial, with 40,000 per arm. The absolute observed difference in colo-rectal mortality between the two arms was 0.03%. The per-protocol analysis (just those people who got tested) was a difference of 0.15%.
That latter figure is the best possible argument for colonoscopy, and no matter how you look at it, it's just not a big difference. Even if you ran a huge trial to get a significant result at these effect sizes, you're still talking about a difference of 15 people per 10,000 (at best) screened. That's a lot of pain and expense for very little gain.