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Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event

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  • Liuquan Sun
  • Xinyuan Song
  • Jie Zhou
  • Lei Liu

Abstract

In many longitudinal studies, repeated measures are often correlated with observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. In this article, we propose a new joint model for the analysis of longitudinal data in the presence of both informative observation times and a dependent terminal event via latent variables. Estimating equation approaches are developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, some graphical and numerical procedures are presented for model checking. Simulation studies demonstrate that the proposed method performs well for practical settings. An application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is provided.

Suggested Citation

  • Liuquan Sun & Xinyuan Song & Jie Zhou & Lei Liu, 2012. "Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 688-700, June.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:498:p:688-700
    DOI: 10.1080/01621459.2012.682528
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    Citations

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    Cited by:

    1. Hongyuan Cao & Mathew M. Churpek & Donglin Zeng & Jason P. Fine, 2015. "Analysis of the Proportional Hazards Model With Sparse Longitudinal Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1187-1196, September.
    2. 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.
    3. D. M. Farewell & C. Huang & V. Didelez, 2017. "Ignorability for general longitudinal data," Biometrika, Biometrika Trust, vol. 104(2), pages 317-326.
    4. Li, Yang & He, Xin & Wang, Haiying & Zhang, Bin & Sun, Jianguo, 2015. "Semiparametric regression of multivariate panel count data with informative observation times," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 209-219.
    5. Lianqiang Qu & Liuquan Sun & Xinyuan Song, 2018. "A Joint Modeling Approach for Longitudinal Data with Informative Observation Times and a Terminal Event," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 609-633, December.
    6. Hangjin Jiang & Wen Su & Xingqiu Zhao, 2020. "Robust estimation for panel count data with informative observation times and censoring times," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 65-84, January.
    7. Janie Coulombe & Erica E. M. Moodie & Robert W. Platt, 2021. "Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies," Biometrics, The International Biometric Society, vol. 77(1), pages 162-174, March.
    8. Xiaoyu Wang & Liuquan Sun, 2023. "Joint modeling of generalized scale-change models for recurrent event and failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 1-33, January.
    9. Yang Li & Xin He & Haiying Wang & Jianguo Sun, 2016. "Regression analysis of longitudinal data with correlated censoring and observation times," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 343-362, July.
    10. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.
    11. 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.
    12. Jie Zhou & Haixiang Zhang & Liuquan Sun & Jianguo Sun, 2017. "Joint analysis of panel count data with an informative observation process and a dependent terminal event," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 560-584, October.
    13. Jie Zhou & Xin Chen & Xinyuan Song & Liuquan Sun, 2021. "A joint modeling approach for analyzing marker data in the presence of a terminal event," Biometrics, The International Biometric Society, vol. 77(1), pages 150-161, March.
    14. He, Haijin & Pan, Deng & Sun, Liuquan & Li, Yimei & Robison, Leslie L. & Song, Xinyuan, 2017. "Analysis of a fixed center effect additive rates model for recurrent event data," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 186-197.
    15. Caleb Weaver & Luo Xiao & Wenbin Lu, 2023. "Functional data analysis for longitudinal data with informative observation times," Biometrics, The International Biometric Society, vol. 79(2), pages 722-733, June.

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