4 ms·

(relative risk = 3.87, 95% CI 1.18–12.7) and liver cancer (relative risk = 6.69, 95% CI 1.71–26.2) I have no domain knowledge here but those CIs are quite larg

(relative risk = 3.87, 95% CI 1.18–12.7) and liver cancer (relative risk = 6.69, 95% CI 1.71–26.2) I have no domain knowledge here but those CIs are quite large and both get close to 1 at the lower edge.

stevenbedrick 3y ago
Without having read the study, but going on experience reading other studies of this general type, that’s a common pattern with CIs derived from predictors in logistic regression-like models (which are often used for this kind of analysis)- the CI calculations often end up having a log in there somewhere which results in higher tails. And a RR of 1.7 is still nothing to sneeze at depending on the base rate of whatever is being discussed… But again, I haven’t read the paper! Just chiming in with some half-forgotten epidemiology. :-)
ludvigk 3y ago
What is this biased way of reading CIs? Are you paid by 3M? Yes, it shows that with 5% probability the risk is only 1.18 - why choose to focus on those 5%? According to the study there is an equal chance that the relative risk is 12.7 times normal, why should we ignore that?
- jasonfarnon 3y ago
  You really think pointing out how large a CI is, is "biased"? Your reading of the CIs is off. It isn't telling you that there's a 5% chance that the real risk is 1.18. It's saying there's a 5% chance the real risk doesn't lie in the CI, and could be anywhere outside the CI, including 1 (no effect). Here's the main point. Increase the significance level slightly, say to 97% (if this is a 95% CI, or 92% if this is a 90%, etc). Then a CI that large and that close to 1 at the left edge will turn into a CI that includes 1. The effect is not significant at all at that level. It wouldn't be saying the true effect is significant with some probability or whatever you seemed to be interpreting CIs to say. It would be saying there's no effect. This is markedly different from the case of cigarettes, where the p-values are astronomically small. Since earlier comments seem to compare these chemical to cigarettes.

stevenbedrick 3y ago

Without having read the study, but going on experience reading other studies of this general type, that’s a common pattern with CIs derived from predictors in logistic regression-like models (which are often used for this kind of analysis)- the CI calculations often end up having a log in there somewhere which results in higher tails. And a RR of 1.7 is still nothing to sneeze at depending on the base rate of whatever is being discussed… But again, I haven’t read the paper! Just chiming in with some half-forgotten epidemiology. :-)

ludvigk 3y ago

What is this biased way of reading CIs? Are you paid by 3M? Yes, it shows that with 5% probability the risk is only 1.18 - why choose to focus on those 5%? According to the study there is an equal chance that the relative risk is 12.7 times normal, why should we ignore that?

jasonfarnon 3y ago
You really think pointing out how large a CI is, is "biased"? Your reading of the CIs is off. It isn't telling you that there's a 5% chance that the real risk is 1.18. It's saying there's a 5% chance the real risk doesn't lie in the CI, and could be anywhere outside the CI, including 1 (no effect). Here's the main point. Increase the significance level slightly, say to 97% (if this is a 95% CI, or 92% if this is a 90%, etc). Then a CI that large and that close to 1 at the left edge will turn into a CI that includes 1. The effect is not significant at all at that level. It wouldn't be saying the true effect is significant with some probability or whatever you seemed to be interpreting CIs to say. It would be saying there's no effect. This is markedly different from the case of cigarettes, where the p-values are astronomically small. Since earlier comments seem to compare these chemical to cigarettes.