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

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  • Liu, Yue
  • Liu, Lei
  • Zhou, Jianhui

Abstract

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.

Suggested Citation

  • Liu, Yue & Liu, Lei & Zhou, Jianhui, 2015. "Joint latent class model of survival and longitudinal data: An application to CPCRA study," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 40-50.
    • Handle: RePEc:eee:csdana:v:91:y:2015:i:c:p:40-50
    DOI: 10.1016/j.csda.2015.05.007
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    References listed on IDEAS

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    Cited by:

    1. Cheng Zheng & Lei Liu, 2022. "Quantifying direct and indirect effect for longitudinal mediator and survival outcome using joint modeling approach," Biometrics, The International Biometric Society, vol. 78(3), pages 1233-1243, September.
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    3. Jiehuan Sun & Jose D. Herazo‐Maya & Philip L. Molyneaux & Toby M. Maher & Naftali Kaminski & Hongyu Zhao, 2019. "Regularized Latent Class Model for Joint Analysis of High‐Dimensional Longitudinal Biomarkers and a Time‐to‐Event Outcome," Biometrics, The International Biometric Society, vol. 75(1), pages 69-77, March.
    4. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

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