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Semiparametric analysis of multivariate panel count data with dependent observation processes and a terminal event

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  • Hui Zhao
  • Yang Li
  • Jianguo Sun

Abstract

This paper considers regression analysis of multivariate panel count data in the presence of some terminal events. Furthermore, both the observation process and the terminal event may be correlated with the recurrent event process of interest. For the problem, we present a semiparametric additive model for the mean function of the recurrent event process and an estimating equation-based inference procedure is developed for the estimation of regression parameters. In the procedure, the inverse survival probability weighting technique is used and the asymptotic properties of the proposed estimators are established. Extensive simulation studies are conducted to evaluate the finite sample properties of the proposed approach, and the results show that the proposed procedures work well for practical situations.

Suggested Citation

  • Hui Zhao & Yang Li & Jianguo Sun, 2013. "Semiparametric analysis of multivariate panel count data with dependent observation processes and a terminal event," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 379-394, June.
    • Handle: RePEc:taf:gnstxx:v:25:y:2013:i:2:p:379-394
    DOI: 10.1080/10485252.2012.758724
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    Cited by:

    1. Wang, Weiwei & Wu, Xianyi & Zhao, Xiaobing & Zhou, Xian, 2018. "Robust variable selection of joint frailty model for panel count data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 60-78.
    2. 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.
    3. John D. Kalbfleisch, 2018. "Discussion of “Survival models and health sequences” by Walter Dempsey and Peter McCullagh," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 585-587, October.
    4. Deng, Shirong & Liu, Kin-yat & Zhao, Xingqiu, 2017. "Semiparametric regression analysis of multivariate longitudinal data with informative observation times," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 120-130.
    5. Guo, Yuanyuan & Sun, Dayu & Sun, Jianguo, 2022. "Inference of a time-varying coefficient regression model for multivariate panel count data," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    6. Zhao, Hui & Sun, Dayu & Li, Gang & Sun, Jianguo, 2019. "Simultaneous estimation and variable selection for incomplete event history studies," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 350-361.
    7. Chunling Wang & Xiaoyan Lin, 2022. "Bayesian Semiparametric Regression Analysis of Multivariate Panel Count Data," Stats, MDPI, vol. 5(2), pages 1-17, May.
    8. Wang, Yijun & Wang, Weiwei & Zhao, Xiaobing, 2022. "Local logarithm partial likelihood estimation of panel count data model with an unknown link function," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
    9. 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.

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