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Additive mixed effect model for recurrent gap time data

Author

Listed:
  • Jieli Ding

    (Wuhan University)

  • Liuquan Sun

    (Chinese Academy of Sciences)

Abstract

Gap times between recurrent events are often of primary interest in medical and observational studies. The additive hazards model, focusing on risk differences rather than risk ratios, has been widely used in practice. However, the marginal additive hazards model does not take the dependence among gap times into account. In this paper, we propose an additive mixed effect model to analyze gap time data, and the proposed model includes a subject-specific random effect to account for the dependence among the gap times. Estimating equation approaches are developed for parameter estimation, and the asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The finite sample behavior of the proposed methods is evaluated through simulation studies, and an application to a data set from a clinic study on chronic granulomatous disease is provided.

Suggested Citation

  • Jieli Ding & Liuquan Sun, 2017. "Additive mixed effect model for recurrent gap time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 223-253, April.
    • Handle: RePEc:spr:lifeda:v:23:y:2017:i:2:d:10.1007_s10985-015-9341-0
    DOI: 10.1007/s10985-015-9341-0
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    References listed on IDEAS

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    1. Yin, Guosheng, 2007. "Model checking for additive hazards model with multivariate survival data," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 1018-1032, May.
    2. Robert L. Strawderman, 2005. "The accelerated gap times model," Biometrika, Biometrika Trust, vol. 92(3), pages 647-666, September.
    3. 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.
    4. Douglas E. Schaubel, 2004. "Regression methods for gap time hazard functions of sequentially ordered multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(2), pages 291-303, June.
    5. 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.
    6. Xuelin Huang & Lei Liu, 2007. "A Joint Frailty Model for Survival and Gap Times Between Recurrent Events," Biometrics, The International Biometric Society, vol. 63(2), pages 389-397, June.
    7. Xianghua Luo & Chiung-Yu Huang & Lan Wang, 2013. "Quantile Regression for Recurrent Gap Time Data," Biometrics, The International Biometric Society, vol. 69(2), pages 375-385, June.
    8. Guosheng Yin & Jianwen Cai, 2004. "Additive hazards model with multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(4), pages 801-818, December.
    9. Zeng, Donglin & Lin, D.Y., 2007. "Semiparametric Transformation Models With Random Effects for Recurrent Events," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 167-180, March.
    10. Jianwen Cai & Donglin Zeng, 2011. "Additive Mixed Effect Model for Clustered Failure Time Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1340-1351, December.
    11. 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.
    12. Cook, Richard J. & Lawless, Jerald F. & Lakhal-Chaieb, Lajmi & Lee, Ker-Ai, 2009. "Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 60-75.
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