Statistical Models and Estimation Methods for Ordinal Longitudinal Data

• Activity Number: 101
• Type: Invited
• Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
• Sponsor: SSC
• Author(s): Claudia Czado* and Claudia Czado+
• Companies: Technische Universitaet Muenchen and Technische Universitaet Muenchen
• Keywords: longitudinal data ; ordinal outcomes ; random effects ; composite likelihood ; MCMC ; migraine severity

Abstract

Longitudinal data with binary and ordinal outcomes often occur 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. To model these data structures 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 and a Bayesian approach using MCMC algorithms is followed. Methods are compared in a simulation study and applied to a large longitudinal study on the determinants of migraine severity. This is joint work with C. Varin and H. Du.