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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

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  • Lei Liu
  • Xuelin Huang

Abstract

Summary. 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.

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  • 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.
    • Handle: RePEc:bla:jorssc:v:58:y:2009:i:1:p:65-81
    DOI: 10.1111/j.1467-9876.2008.00641.x
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    Cited by:

    1. M. H. Hof & J. Z. Musoro & R. B. Geskus & G. H. Struijk & I. J. M. ten Berge & A. H. Zwinderman, 2017. "Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2756-2777, November.
    2. Na Cai & Wenbin Lu & Hao Helen Zhang, 2012. "Time-Varying Latent Effect Model for Longitudinal Data with Informative Observation Times," Biometrics, The International Biometric Society, vol. 68(4), pages 1093-1102, December.
    3. Gongjun Xu & Sy Han Chiou & Chiung-Yu Huang & Mei-Cheng Wang & Jun Yan, 2017. "Joint Scale-Change Models for Recurrent Events and Failure Time," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 794-805, April.
    4. 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.
    5. Shanshan Li, 2016. "Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 145-160, January.
    6. Yang-Jin Kim, 2014. "Regression analysis of recurrent events data with incomplete observation gaps," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1619-1626, July.
    7. Melkamu Molla Ferede & Samuel Mwalili & Getachew Dagne & Simon Karanja & Workagegnehu Hailu & Mahmoud El-Morshedy & Afrah Al-Bossly, 2022. "A Semiparametric Bayesian Joint Modelling of Skewed Longitudinal and Competing Risks Failure Time Data: With Application to Chronic Kidney Disease," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
    8. Qing Cai & Mei‐Cheng Wang & Kwun Chuen Gary Chan, 2017. "Joint modeling of longitudinal, recurrent events and failure time data for survivor's population," Biometrics, The International Biometric Society, vol. 73(4), pages 1150-1160, December.
    9. Dimitris Rizopoulos & Laura A. Hatfield & Bradley P. Carlin & Johanna J. M. Takkenberg, 2014. "Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1385-1397, December.

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