Individual Migraine Risk Management using Binary State Space Mixed Models.
Collaborative Research Center 386, Discussion Paper 235
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.