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 autoregressive ordinal probit (AOP)
model of M¨uller and Czado (2005) 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 windchill and sunshine length,
but not humidity 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.