That looks like it could prove really useful / interesting; thanks for linking.
I guess the entry requirements for beta are strict because they're trying to keep to a small set of variables for the people to check? It would have been really interesting to spy in on though. Regarding the China study, it sounds either like there was no effort to control for other obvious/statistically-true correlates or that there is no possible overlap at all to abstract a controlled comparison from. A fraction of that data might be useful (all data is useful! ...yum!). I think with sufficient (though perhaps improbably large) sample size even user-submitted data with large amounts of noise becomes useful. Any empirical paradigm more open and faster than the current is bound to be a good thing, even despite inaccuracy, for reasons of sheer brute force.
At least with user submitted noisy data you have individual data points, and potential to track individuals over time... unlike the China Study where entire communities were just averaged into a single point.
There's some usable information in the China Study, but not as much as people think... it's being touted as "proof" that all animal-based foods cause cancer (in a popular diet book by the primary investigator Dr. Campbell) because the two were well correlated in the data, when it's nothing of the sort.
Nutrition is a case where we have to try to make the best possible use of the data we have no matter how terrible, because we have to eat something now to sustain us while we plan and conduct more experiments.
I want to apply Bayes theorem to make rational health decisions from relatively weak data. I am generally wondering how one can synthesize historical human experiences with incomplete scientific data, in order to make risk-adverse and healthy decisions about human nutrition given limited research.
Example question/hypothesis: Does gluten cause health problems (ie exhibit chronic toxicity) in non-coeliac humans? Is there enough evidence to suggest that avoiding gluten might be a prudent risk-adverse decision for non-coeliacs?
We have some (mostly in vitro) scientific data suggesting that gluten may cause health problems in non-coeliac humans (such as these articles http://evolvify.com/the-case-against-gluten-medical-journal-references/). Let's say for the sake of arguing, that I can somehow convert these studies into a non-unity likelihood ratio for gluten toxicity in humans (although suggestions are welcome here too).
However, we also have prior information that a population of humans has been consuming gluten containing foods for at least 10,000 years, without any blatantly obvious toxic effects. Is there some way to convert this observation (and observations like this) into a prior probability distribution?