Joint analysis of correlated repeated measures and recurrent events processes in the presence of death, with application to a study on acquired immune deficiency syndrome

Lei Liu*, Xuelin Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

In many longitudinal studies, we observe two correlated processes: a repeated measures process and a recurrent events process, both subject to a dependent terminal event. For example, in the 'Terry Beirn community programs for clinical research on AIDS' (CPCRA) study, higher CD4 cell counts are associated with lower risk of recurrent opportunistic diseases. They are also correlated with mortality, e.g. higher CD4 cell repeated measures and a lower rate of opportunistic disease imply better survival for patients infected with the human immunodeficiency virus. We propose a joint random-effects model for the three correlated outcomes. The correlation is modelled by conditioning on shared random effects. Covariate effects can be taken into account in the model. Maximum likelihood estimation and inference are carried out through a Gaussian quadrature technique, assuming piecewise constant baseline hazards for recurrent events and death. The model can be fitted conveniently by Gaussian quadrature tools, e.g. SAS procedure NLMIXED. Simulation studies show that the estimation method yields satisfactory results. We apply this method to the CPCRA data.

Original languageEnglish (US)
Pages (from-to)65-81
Number of pages17
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume58
Issue number1
DOIs
StatePublished - Feb 2009

Keywords

  • Frailty model
  • Informative censoring
  • Longitudinal data analysis
  • Mixed effects model
  • Proportional hazards model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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