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A semiparametric likelihood†based method for regression analysis of mixed panel†count data

Author

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  • Liang Zhu
  • Ying Zhang
  • Yimei Li
  • Jianguo Sun
  • Leslie L. Robison

Abstract

Panel†count data arise when each study subject is observed only at discrete time points in a recurrent event study, and only the numbers of the event of interest between observation time points are recorded (Sun and Zhao, 2013). However, sometimes the exact number of events between some observation times is unknown and what we know is only whether the event of interest has occurred. In this article, we will refer this type of data to as mixed panel†count data and propose a likelihood†based semiparametric regression method for their analysis by using the nonhomogeneous Poisson process assumption. However, we establish the asymptotic properties of the resulting estimator by employing the empirical process theory and without using the Poisson assumption. Also, we conduct an extensive simulation study, which suggests that the proposed method works well in practice. Finally, the method is applied to a Childhood Cancer Survivor Study that motivated this study.

Suggested Citation

  • Liang Zhu & Ying Zhang & Yimei Li & Jianguo Sun & Leslie L. Robison, 2018. "A semiparametric likelihood†based method for regression analysis of mixed panel†count data," Biometrics, The International Biometric Society, vol. 74(2), pages 488-497, June.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:488-497
    DOI: 10.1111/biom.12774
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    References listed on IDEAS

    as
    1. Jianguo Sun, 2003. "A nonparametric test for panel count data," Biometrika, Biometrika Trust, vol. 90(1), pages 199-208, March.
    2. Jianguo Sun & Xingwei Tong & Xin He, 2007. "Regression Analysis of Panel Count Data with Dependent Observation Times," Biometrics, The International Biometric Society, vol. 63(4), pages 1053-1059, December.
    3. Gang Cheng & Ying Zhang & Liqiang Lu, 2011. "Efficient algorithms for computing the non and semi-parametric maximum likelihood estimates with panel count data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 567-579.
    4. Liang Zhu & Hui Zhao & Jianguo Sun & Wendy Leisenring & Leslie L. Robison, 2015. "Regression analysis of mixed recurrent-event and panel-count data with additive rate models," Biometrics, The International Biometric Society, vol. 71(1), pages 71-79, March.
    5. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    6. J. Sun & L. J. Wei, 2000. "Regression analysis of panel count data with covariate‐dependent observation and censoring times," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 293-302.
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    Citations

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    Cited by:

    1. Ryan Sun & Dayu Sun & Liang Zhu & Jianguo Sun, 2023. "Regression analysis of general mixed recurrent event data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 807-822, October.
    2. Yang Wang & Zhangsheng Yu, 2022. "A kernel regression model for panel count data with nonparametric covariate functions," Biometrics, The International Biometric Society, vol. 78(2), pages 586-597, June.
    3. Yimei Li & Liang Zhu & Lei Liu & Leslie L. Robison, 2021. "Regression Analysis of Mixed Panel-Count Data with Application to Cancer Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 178-195, April.

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