Fighting Migraine with Multilevel Modeling

Hal Pashler writes:

Ed Vul and I are working on something that, although less exciting than the struggle against voodoo correlations in fMRI :-) might interest you and your readers. The background is this: we have been struck for a long time by how many people get frustrated and confused trying to figure out whether something they are doing/eating/etc is triggering something bad, whether it be migraine headaches, children’s tantrums, arthritis pains, or whatever. It seems crazy to try to do such computations in one’s head–and the psychological literature suggests people must be pretty bad at this kind of thing–but what’s the alternative? We are trying to develop one alternative approach–starting with migraine as a pilot project.

We created a website that migraine sufferers can sign up for. The users select a list of factors that they think might be triggering their headaches (eg drinking red wine, eating stinky cheese, etc.–the website suggests a big list of candidates drawn from the migraine literature). Then, every day the user is queried about how much they were exposed to each of these potential triggers that day, as well as whether they had a headache. After some months, the site begins to analyze the user’s data to try to figure out which of these triggers–if any–are actually causing headaches.

Our approach uses multilevel logistic regression as in Gelman and Hill, and or Gelman and Little (1997), and we use parametric bootstrapping to obtain posterior predictive confidence intervals to provide practical advice (rather than just ascertain the significance of effects). At the start the population-level hyperparameters on individual betas start off uninformative (uniform), but as we get data from an adequate number of users (we’re not there quite yet), we will be able to pool information across users to provide appropriate population-level priors on the regression coefficients for each possible trigger factor for each person. The approach is outlined in this FAQ item.

Looks cool to me.