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Joint frailty models for zero-inflated recurrent events in the presence of a terminal event

Author

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  • Lei Liu
  • Xuelin Huang
  • Alex Yaroshinsky
  • Janice N. Cormier

Abstract

type="main" xml:lang="en"> Recurrent event data arise frequently in longitudinal medical studies. In many situations, there are a large portion of subjects without any recurrent events, manifesting the “zero-inflated” nature of the data. Some of the zero events may be “structural zeros” as patients are unsusceptible to recurrent events, while others are “random zeros” due to censoring before any recurrent events. On the other hand, there often exists a terminal event which may be correlated with the recurrent events. In this article, we propose two joint frailty models for zero-inflated recurrent events in the presence of a terminal event, combining a logistic model for “structural zero” status (Yes/No) and a joint frailty proportional hazards model for recurrent and terminal event times. The models can be fitted conveniently in SAS Proc NLMIXED. We apply the methods to model recurrent opportunistic diseases in the presence of death in an AIDS study, and tumor recurrences and a terminal event in a sarcoma study.

Suggested Citation

  • Lei Liu & Xuelin Huang & Alex Yaroshinsky & Janice N. Cormier, 2016. "Joint frailty models for zero-inflated recurrent events in the presence of a terminal event," Biometrics, The International Biometric Society, vol. 72(1), pages 204-214, March.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:1:p:204-214
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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. 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.
    3. Dongxiao Han & Xiaogang Su & Liuquan Sun & Zhou Zhang & Lei Liu, 2020. "Variable selection in joint frailty models of recurrent and terminal events," Biometrics, The International Biometric Society, vol. 76(4), pages 1330-1339, December.
    4. Richard Tawiah & Geoffrey J. McLachlan & Shu Kay Ng, 2020. "A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction," Biometrics, The International Biometric Society, vol. 76(3), pages 753-766, September.

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