Biostats question

[Apoplexy__]

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Quick Q: In the case that you have a CI that extends from, say, 0.8 to 3.5, what exactly can you say about the causality you're trying to establish? Is it:
-No causality can be shown
-Causality was shown, but you don't know if there was a <5% chance something else was responsible for causality

I ask because I got a biostats Q where the right answer was "the sample size was too small" in favor of "the result proves no causality".

 

[Missorleans]

1) correlation does not = causality
2) I assume this is the CI for an RR or OR, in which case, it includes 1, so the results are not significant.
So in the answer choices, No causality can be shown would be a correct answer.

 

[Apoplexy__]

1) correlation does not = causality
2) I assume this is the CI for an RR or OR, in which case, it includes 1, so the results are not significant.
So in the answer choices, No causality can be shown would be a correct answer.

Sorry, yeah it was an odds ratio.

The correct answer with this was actually "the sample size was small", which is reasonable if you saw the question. I'm just wondering why you can't say "the result proves no causality". Is it because you still may have causality with an OR from 0.8 - 3.5 just no statistical correlation?

 

[Missorleans]

Oh, yeah, I guess you can't say for sure there is or is not causality, since OR doesn't tell you anything about causality. I guess it's a good distractor, I'd have picked it if I didn't see anything better.

 

[Apoplexy__]

Oh, yeah, I guess you can't say for sure there is or is not causality, since OR doesn't tell you anything about causality. I guess it's a good distractor, I'd have picked it if I didn't see anything better.

Ok, thanks! I appreciate the help.

 

[orchitisphlebitis]

Pretty much what Missorleans said.

If the CI for the OR does contain one, all that means is that you fail to reject the null hypothesis. I don't think it's correct to draw more conclusions beyond that. And think about it, even if the CI DIDN'T include 1, (thus meaning there is a significant difference between the two data sets beyond just chance), you still couldn't necessarily infer causality, as confounders could account for that (what would appear to be) causal relationship.

Does that make sense? (If anybody more versed in biostats than me notices anything wrong with the above, please say so haha).