Modeling migraine severity with autoregressive ordered probit models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Paper ; No. 463

Classification and Topics

• Subject: Proportional odds ; autoregressive component ; ordinal valued time series ; regression ; Markov Chain Monte Carlo (MCMC) ; deviance ; Bayes factor

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Czado, Claudia ; Heyn, Anette ; Müller, Gernot J.
• Event: Veröffentlichung
• (who): Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
• (where): München
• (when): 2005

Further information

• DOI: doi:10.5282/ubm/epub.1832
• Handle: http://hdl.handle.net/10419/31114
• URN: urn:nbn:de:bvb:19-epub-1832-6
• Last update: 10.03.2025, 11:45 AM CET