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Robust estimation for panel count data with informative observation times and censoring times

Author

Listed:
  • Hangjin Jiang

    () (ZheJiang University
    The Chinese University of Hong Kong)

  • Wen Su

    (Haitong International Securities Group)

  • Xingqiu Zhao

    (The Hong Kong Polytechnic University)

Abstract

We consider the semiparametric regression of panel count data occurring in longitudinal follow-up studies that concern occurrence rate of certain recurrent events. The analysis of panel count data involves two processes, i.e, a recurrent event process of interest and an observation process controlling observation times. However, the model assumptions of existing methods, such as independent censoring time and Poisson assumption, are restrictive and questionable. In this paper, we propose new joint models for panel count data by considering both informative observation times and censoring times. The asymptotic normality of the proposed estimators are established. Numerical results from simulation studies and a real data example show the advantage of the proposed method.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:lifeda:v:26:y:2020:i:1:d:10.1007_s10985-018-09457-7
    DOI: 10.1007/s10985-018-09457-7
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    References listed on IDEAS

    as
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