Earlier quoted context omitted.
> Building a double-blind clinical trial regarding changing people's diets over their lifetime and observing cardiovascular mortality is very hard. But it is the only diet research that can actually generate knowledge. The studies you cite are nice and all, but the first two are for studies that measured outcomes after 6-8 weeks . If you assume an average lifespan of 70 years, the longest study represented ~1/450th o…
> But it is the only diet research that can actually generate knowledge. > The studies you cite are nice and all, but the first two are for studies that measured outcomes after 6-8 weeks. If you assume an average lifespan of 70 years, the longest study represented ~1/450th of a human lifespan. And, as you mention, they only measure what we think are valid indicators, not actual outcomes! And the third study may contr…
The studies all tell us nothing about the long term implications of diet. The long study tells us nothing because it is purely correlational. The short studies tell us nothing because they are not measuring the thing we care about (long term health outcomes). You can't somehow generate knowledge by combining two types of studies which each are incapable of generating knowledge about the thing we care about.
> I never claimed that meat was bad, only that leafy vegetables were good.
Yeah, I clearly was hallucinating. Nowhere did you mention red meat. And yet I rambled on about it for a paragraph. :-) Sorry!
But:
> I find it almost incredible that your argument against the nutritional value of leafy vegetables is that there aren't enough double blind clinical trials, and then your defense of red meat uses a time correlation that doesn't adjust for any confounding variables at all.
That's because correlation cannot demonstrate causation, but it can demonstrate the lack of causation. It's all about confounding variables, as you point out. If you have two trend lines which correlate, there can always be a third, unmeasured variable which is actually causing the two measured variables. However, if you have trend lines which do not correlate, then the only way they can be related is by some extremely convoluted chain of causation. Which, I guess, is technically possible, but rapidly approaches 0 probability.
In other words, epidemiology is very useful for narrowing the possibly explanations for some phenomena, and it can suggest places where we should continue focusing our attention, but it can't actually tell us if we've found the right culprit.