Modeling migraine severity with autoregressive ordered probit models

This paper considers the problem of modeling migraine severity
assessments and their dependence on weather and time
characteristics. Since ordinal severity measurements arise from a
single patient dependencies among the measurements have to be
accounted for. For this the autore- gressive ordinal probit (AOP)
model of Müller and Czado (2004) is utilized and fitted by a
grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler.
Initially, covariates are selected using proportional odds models
ignoring this dependency. Model fit and model comparison are
discussed. The analysis shows that humidity, windchill, sunshine
length and pressure differences have an effect in addition to a
high dependence on previous measurements. A comparison with
proportional odds specifications shows that the AOP models are
preferred.