Latent Ornstein-Uhlenbeck models for Bayesian analysis of multivariate longitudinal categorical responses

Trung Dung Tran*, Emmanuel Lesaffre, Geert Verbeke, Joke Duyck

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Web of Science)

Abstract

We propose a Bayesian latent Ornstein-Uhlenbeck (OU) model to analyze unbalanced longitudinal data of binary and ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the evolution of such latent variables when they continuously change over time. Existing approaches are limited to data collected at regular time intervals. Our proposal makes use of an OU process for the latent variables to overcome this limitation. We show that assuming real eigenvalues for the drift matrix of the OU process, as is frequently done in practice, can lead to biased estimates and/or misleading inference when the true process is oscillating. In contrast, our proposal allows for both real and complex eigenvalues. We illustrate our proposed model with a motivating dataset, containing patients with amyotrophic lateral sclerosis disease. We were interested in how bulbar, cervical, and lumbar functions evolve over time.
Original languageEnglish
Pages (from-to)689-701
Number of pages13
JournalBiometrics
Volume77
Issue number2
Early online dateJun 2020
DOIs
Publication statusPublished - Jun 2021
Externally publishedYes

Keywords

  • Bayesian modeling
  • Ornstein-Uhlenbeck process
  • Latent variable
  • Multivariate longitudinal data analysis
  • Oscillating and nonoscillating processes

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