Analysis of longitudinal data in the presence of informative observational times and a dependent terminal event, with application to medical cost data

Lei Liu*, Xuelin Huang, John O'Quigley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

93 Scopus citations

Abstract

In longitudinal observational studies, repeated measures are often taken at informative observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. For example, patients in poorer health are more likely to seek medical treatment and their medical cost for each visit tends to be higher. They are also subject to a higher mortality rate. In this article, we propose a random effects model of repeated measures in the presence of both informative observation times and a dependent terminal event. Three submodels are used, respectively, for (1) the intensity of recurrent observation times, (2) the amount of repeated measure at each observation time, and (3) the hazard of death. Correlated random effects are incorporated to join the three submodels. The estimation can be conveniently accomplished by Gaussian quadrature techniques, e.g., SAS Proc NLMIXED. An analysis of the cost-accrual process of chronic heart failure patients from the clinical data repository at the University of Virginia Health System is presented to illustrate the proposed method.

Original languageEnglish (US)
Pages (from-to)950-958
Number of pages9
JournalBiometrics
Volume64
Issue number3
DOIs
StatePublished - Sep 2008

Keywords

  • Frailty model
  • Informative drop-out
  • Longitudinal medical costs
  • Piecewise constant baseline hazard
  • Proportional hazards model
  • Recurrent marker
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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