Joint latent class model of survival and longitudinal data: An application to CPCRA study

Yue Liu*, Lei Liu, Jianhui Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


There has been an increasing interest in the joint analysis of repeated measures and time to event data. In many studies, there could also exist heterogeneous subgroups. Thus a new model is proposed for the joint analysis of longitudinal and survival data with underlying subpopulations identified by latent class model. Within each latent class, a joint model of longitudinal and survival data with shared random effects is adopted. The proposed model is applied to Terry Beirn Community Programs for Clinical Research on AIDS study (CPCRA) to characterize the underlying heterogeneity of the cohort and to study the relation between longitudinal CD4 measures and time to death. The proposed model is desirable when the heterogeneity among subjects cannot be ignored and both the longitudinal and survival outcomes are of interest.

Original languageEnglish (US)
Pages (from-to)40-50
Number of pages11
JournalComputational Statistics and Data Analysis
StatePublished - Jun 20 2015


  • Counting process
  • Dependent censoring
  • Frailty model
  • Gaussian quadrature
  • Informative dropout
  • Mixed effects model
  • Mixture model
  • Proportional hazards model
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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