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Analysis of a fixed center effect additive rates model for recurrent event data

Author

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  • He, Haijin
  • Pan, Deng
  • Sun, Liuquan
  • Li, Yimei
  • Robison, Leslie L.
  • Song, Xinyuan

Abstract

A center effect additive rates model is suggested to analyze recurrent event data. The proposed model is a useful alternative to the center effect proportional rates model and provides a direct interpretation of parameters. The traditional estimation methods treat the centers as categorical variables, and they comprise many parameters when the number of centers is large and thus may not be feasible in many situations. An estimation method based on the difference in the observed to the expected number of recurrent events is recommended to address the deficiency of the traditional method. The asymptotic properties of the proposed estimator are established. We develop a goodness-of-fit test for model checking. Simulations are conducted to evaluate the small sample performance and show the computational advantage of the suggested method. The proposed methodology is applied to the Childhood Cancer Survivor Study.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:112:y:2017:i:c:p:186-197
    DOI: 10.1016/j.csda.2017.03.003
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    References listed on IDEAS

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    1. Yining Ye & John D. Kalbfleisch & Douglas E. Schaubel, 2007. "Semiparametric Analysis of Correlated Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 63(1), pages 78-87, March.
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    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. Dandan Liu & Douglas E. Schaubel & John D. Kalbfleisch, 2012. "Computationally Efficient Marginal Models for Clustered Recurrent Event Data," Biometrics, The International Biometric Society, vol. 68(2), pages 637-647, June.
    5. Donglin Zeng & Jianwen Cai, 2010. "A semiparametric additive rate model for recurrent events with an informative terminal event," Biometrika, Biometrika Trust, vol. 97(3), pages 699-712.
    6. Liuquan Sun & Xinyuan Song & Jie Zhou & Lei Liu, 2012. "Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 688-700, June.
    7. Lin D Y & Ying Z, 2001. "Semiparametric and Nonparametric Regression Analysis of Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 103-126, March.
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    Cited by:

    1. Brenière, Léa & Doyen, Laurent & Bérenguer, Christophe, 2020. "Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    2. 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.

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