Joint analysis of longitudinal data with informative observation times and a dependent terminal event

Liuquan Sun*, Xinyuan Song, Jie Zhou, Lei Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

In many longitudinal studies, repeated measures are often correlated with observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. In this article, we propose a new joint model for the analysis of longitudinal data in the presence of both informative observation times and a dependent terminal event via latent variables. Estimating equation approaches are developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, some graphical and numerical procedures are presented for model checking. Simulation studies demonstrate that the proposed method performs well for practical settings. An application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is provided.

Original languageEnglish (US)
Pages (from-to)688-700
Number of pages13
JournalJournal of the American Statistical Association
Volume107
Issue number498
DOIs
StatePublished - Jan 1 2012

Keywords

  • Dependent observation times
  • Estimating equations
  • Informative drop-out
  • Joint modeling
  • Latent variables
  • Longitudinal medical cost

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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