Serial correlation structures in latent linear mixed models for analysis of multivariate longitudinal ordinal responses

• Trung Dung Tran ^[1] ; Emmanuel Lesaffre ; Geert Verbeke ; Geert Molenberghs
  1. [1] KU Leuven

     KU Leuven

     Arrondissement Leuven, Bélgica

     
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.) , Dae-Jin Lee (ed. lit.) , Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 238-241
• Idioma: inglés
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• Resumen

  • We propose a latent linear mixed model to analyze multivariate longitudinal data of multiple ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the latent level where the e ects of observed covariates on the latent variables are of interest. We incorporate serial correlation into the variance component rather than assuming independent residuals.

    We show that misleading inference may be drawn when misspecifying the variance component. We apply our proposed model to examine the treatment e ect on patients with the amyotrophic lateral sclerosis (ALS) disease. The result shows that the treatment can slow down the decline of cervical and lumbar functions.