A mixed autoregressive probit model for ordinal longitudinal data

作者：Varin Cristiano^*; Czado Claudia

来源：Biostatistics, 2010, 11(1): 127-138.

DOI：10.1093/biostatistics/kxp042

• Autoregressive errors
• Composite likelihood
• Longitudinal data
• Migraine severity
• Mixed models
• Ordinal probit
• Pairwise likelihood

Longitudinal data with binary and ordinal outcomes routinely appear in medical applications. Existing methods are typically designed to deal with short measurement series. In contrast, modern longitudinal data can result in large numbers of subject-specific serial observations. In this framework, we consider multivariate probit models with random effects to capture heterogeneity and autoregressive terms for describing the serial dependence. Since likelihood inference for the proposed class of models is computationally burdensome because of high-dimensional intractable integrals, a pseudolikelihood approach is followed. The methodology is motivated by the analysis of a large longitudinal study on the determinants of migraine severity.

• 出版日期2010-1