A mixed autoregressive probit model for ordinal longitudinal data.
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.
Varin
Cristiano
C
Department of Statistics, University Ca' Foscari, San Giobbe, Cannaregio 873, I-30121 Venice, Italy. sammy@unive.it
Czado
Claudia
C
D000465:Algorithms; D000700:Analgesics / Q000627:therapeutic use; D001274:Atmospheric Pressure; D056808:Biostatistics / Q000379:methods*; D003198:Computer Simulation; D004522:Educational Status; D006801:Humans; D006813:Humidity; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008499:Medical Records; D008881:Migraine Disorders / Q000175:diagnosis / Q000188:drug therapy / Q000453:epidemiology; D015233:Models, Statistical; D015999:Multivariate Analysis; D011336:Probability; D016008:Statistical Distributions; D011795:Surveys and Questionnaires; D013696:Temperature; D014887:Weather
D000700:Analgesics