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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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    1. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    2. Lin H. & Turnbull B. W. & McCulloch C. E. & Slate E. H., 2002. "Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data: Application to Longitudinal Prostate-Specific Antigen Readings and Prostate Cancer," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 53-65, March.
    3. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
    4. Caroline Beunckens & Geert Molenberghs & Geert Verbeke & Craig Mallinckrodt, 2008. "A Latent-Class Mixture Model for Incomplete Longitudinal Gaussian Data," Biometrics, The International Biometric Society, vol. 64(1), pages 96-105, March.
    5. Hélène Jacqmin-Gadda & Cécile Proust-Lima & Jeremy M.G. Taylor & Daniel Commenges, 2010. "Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model," Biometrics, The International Biometric Society, vol. 66(1), pages 11-19, March.
    6. Lei Liu & Xuelin Huang, 2009. "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," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 65-81, February.
    7. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    8. Klaus Larsen, 2004. "Joint Analysis of Time-to-Event and Multiple Binary Indicators of Latent Classes," Biometrics, The International Biometric Society, vol. 60(1), pages 85-92, March.
    9. Liu, Lei & Strawderman, Robert L. & Cowen, Mark E. & Shih, Ya-Chen T., 2010. "A flexible two-part random effects model for correlated medical costs," Journal of Health Economics, Elsevier, vol. 29(1), pages 110-123, January.
    10. Francisca Galindo Garre & Aeilko H. Zwinderman & Ronald B. Geskus & Yvo W. J. Sijpkens, 2008. "A joint latent class changepoint model to improve the prediction of time to graft failure," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 299-308, January.
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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.
    2. 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.
    3. Beom Seuk Hwang & Zhen Chen & Germaine M. Buck Louis & Paul S. Albert, 2019. "A Bayesian multi‐dimensional couple‐based latent risk model with an application to infertility," Biometrics, The International Biometric Society, vol. 75(1), pages 315-325, 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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