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Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study

Author

Listed:
  • Liang Zhu

    (The University of Texas Health Science Center at Houston)

  • Sangbum Choi

    (Korea University)

  • Yimei Li

    (St. Jude Children’s Research Hospital)

  • Xuelin Huang

    (The University of Texas MD Anderson Cancer Center)

  • Jianguo Sun

    (University of Missouri)

  • Leslie L. Robison

    (St. Jude Children’s Research Hospital)

Abstract

In long-term follow-up studies on recurrent events, the observation patterns may not be consistent over time. During some observation periods, subjects may be monitored continuously so that each event occurence time is known. While during the other observation periods, subjects may be monitored discretely so that only the number of events in each period is known. This results in mixed recurrent-event and panel-count data. In these data, there is dependence among within-subject events. Furthermore, if the data are collected from multiple centers, then there is another level of dependence among within-center subjects. Literature exists for clustered recurrent-event data, but not for clustered mixed recurrent-event and panel-count data. Ignoring the cluster effect may lead to less efficient analysis. In this paper, we present a marginal modeling approach to take into account the cluster effect and provide asymptotic distributions of the resulting regression parameters. Our simulation study demonstrates that this approach works well for practical situations. It was applied to a study comparing the hospitalization rates between childhood cancer survivors and healthy controls, with data collected from 26 medical institutions across North America during more than 20 years of follow-up.

Suggested Citation

  • Liang Zhu & Sangbum Choi & Yimei Li & Xuelin Huang & Jianguo Sun & Leslie L. Robison, 2020. "Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 820-832, October.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:4:d:10.1007_s10985-020-09500-6
    DOI: 10.1007/s10985-020-09500-6
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    References listed on IDEAS

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    1. Mei-Cheng Wang & Ying-Qing Chen, 2000. "Nonparametric and Semiparametric Trend Analysis for Stratified Recurrence Times," Biometrics, The International Biometric Society, vol. 56(3), pages 789-794, September.
    2. Sun, Liuquan & Zhu, Liang & Sun, Jianguo, 2009. "Regression analysis of multivariate recurrent event data with time-varying covariate effects," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2214-2223, November.
    3. 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.
    4. He, Haijin & Pan, Deng & Sun, Liuquan & Li, Yimei & Robison, Leslie L. & Song, Xinyuan, 2017. "Analysis of a fixed center effect additive rates model for recurrent event data," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 186-197.
    5. 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.
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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.

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