Individual Migraine Risk Management using Binary State Space Mixed Models

In this paper binary state space mixed models of Czado and Song
(2001) are applied to construct individual risk profiles based on a
daily dairy of a migraine headache sufferer. These models allow for
the modeling of a dynamic structure together with parametric
covariate effects. Since the analysis is based on posterior
inference using Markov Chain Monte Carlo (MCMC) methods, Bayesian
model fit and model selection criteria are adapted to these binary
state space mixed models. It is shown how they can be used to
select an appropriate model, for which the probability of a
headache today given the occurrence or nonoccurrence of a headache
yesterday in dependency on weather conditions such as windchill and
humidity can be estimated. This can provide the basis for pain
management of such patients.