I've often wondered if a large-userbase data collecting website could help solve problems like this by looking for very weak statistical correlations among coinciding events over large datafields. I.e. see how often people self-report eating X, see how often people self-report feeling Y, see how often one precedes the other and when they happen independently. The function to users would be letting them track their own actions (e.g. diet, health, etc) according to preset (or high-karma member-submitted) input:data -sets. I should think with members in the thousands such a thing would become useful. Especially were the service entangled with some social app to get users and some very good statistics processing to get results. Does anything like this at all exist? (Any obvious ideas why it doesn't, barring there possibly being lack of incentive to use it, lack of an incentive for a company/person to program it?)
Yes, it exists: http://genomera.com
They're actively running experiments and collecting data but are in "beta testing" and are very exclusive on whom they allow to join. I'm disappointed they didn't choose me when I filled out their request for a beta invite.
A huge problem with collecting data like this in the US population, is that everyone has a similar diet. There's so few people totally excluding gluten, you can't expect to measure it's effects with epidemiological diet surveys: you need to actually do a controlled trial where you tell people ...
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?