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Proportional rates model for recurrent event data with intermittent gaps and a terminal event

Author

Listed:
  • Jin Jin

    (University of Science and Technology Beijing)

  • Xinyuan Song

    (The Chinese University of Hong Kong)

  • Liuquan Sun

    (University of Chinese Academy of Sciences)

  • Pei-Fang Su

    (National Cheng Kung University)

Abstract

Recurrent events are common in medical practice or epidemiologic studies when each subject experiences a particular event repeatedly over time. In some long-term observations of recurrent events, a terminal event such as death may exist in recurrent event data. Meanwhile, some inspected subjects will withdraw from a study for some time for various reasons and then resume, which may happen more than once. The period between the subject leaving and returning to the study is called an intermittent gap. One naive method typically ignores gaps and treats the events as usual recurrent events, which could result in misleading estimation results. In this article, we consider a semiparametric proportional rates model for recurrent event data with intermittent gaps and a terminal event. An estimation procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. Simulation studies demonstrate that the proposed estimators perform satisfactorily compared to the naive method that ignores gaps. A diabetes study further shows the utility of the proposed method.

Suggested Citation

  • Jin Jin & Xinyuan Song & Liuquan Sun & Pei-Fang Su, 2025. "Proportional rates model for recurrent event data with intermittent gaps and a terminal event," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(1), pages 126-148, January.
    • Handle: RePEc:spr:lifeda:v:31:y:2025:i:1:d:10.1007_s10985-024-09644-9
    DOI: 10.1007/s10985-024-09644-9
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