The answer given was that according to the differential measurement lecture, differential measurement of exposure has to
be dependent on the outcome for there to be error, that’s not going to happen for cohort study cause it’s not till years later
that the outcome is known.
How are exposures set in this study? What if the final outcome depends on an unobserved cause (health status maybe?), and that cause also influences an intermediate outcome that does determine the measurement of some exposure along the way (via doctor assigning the exposure based on it, maybe?)
Or am I misunderstanding the question? (This is entirely possible, I don’t fully understand epi lingo, I just construct counterexamples via d-separation/d-connection in graphs directly).
Where are you taking this class, if you don’t mind me asking?
In cohort studies, the experimenter doesn’t set exposures
Yes I understand, but somehow they are set (maybe by Nature?) The real question I was getting at is whether they were randomized at all, or pseudo-randomized somehow. I was guessing not, so you get time-varying confounding issues alluded to in my earlier post.
So by unobserved you’re referring to say, self report of health status?
Well, if it’s self-report you observe a proxy. I meant actually unobserved (e.g. we don’t even ask them, but the variable is still there and relevant).
In epi this is meets the causal pathways definition for a confounder, if I’m not mistaken.
You are right, in this case, but should be careful about the definition of a confounder, see:
Did you mean “confounding” rather than “confounder”? The difference is important (the former is much easier to define, it is just related to what is called conditional ignorability in epi, the latter is quite tricky).
Is there another question you might be getting at that I can answer without identifying myself?
How are exposures set in this study? What if the final outcome depends on an unobserved cause (health status maybe?), and that cause also influences an intermediate outcome that does determine the measurement of some exposure along the way (via doctor assigning the exposure based on it, maybe?)
Or am I misunderstanding the question? (This is entirely possible, I don’t fully understand epi lingo, I just construct counterexamples via d-separation/d-connection in graphs directly).
Where are you taking this class, if you don’t mind me asking?
this was an unhelpful comment, removed and replaced by this comment
Yes I understand, but somehow they are set (maybe by Nature?) The real question I was getting at is whether they were randomized at all, or pseudo-randomized somehow. I was guessing not, so you get time-varying confounding issues alluded to in my earlier post.
Well, if it’s self-report you observe a proxy. I meant actually unobserved (e.g. we don’t even ask them, but the variable is still there and relevant).
You are right, in this case, but should be careful about the definition of a confounder, see:
http://arxiv.org/abs/1304.0564
Did you mean “confounding” rather than “confounder”? The difference is important (the former is much easier to define, it is just related to what is called conditional ignorability in epi, the latter is quite tricky).
No, that was enough information, thank you.