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Additive hazards model with multivariate failure time data

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  • Guosheng Yin
  • Jianwen Cai

Abstract

Marginal additive hazards models are considered for multivariate survival data in which individuals may experience events of several types and there may also be correlation between individuals. Estimators are proposed for the parameters of such models and for the baseline hazard functions. The estimators of the regression coeffcients are shown asymptotically to follow a multivariate normal distribution with a sandwich-type covariance matrix that can be consistently estimated. The estimated baseline and subject-specific cumulative hazard processes are shown to converge weakly to a zero-mean Gaussian random field. The weak convergence properties for the corresponding survival processes are established. A resampling technique is proposed for constructing simultaneous confidence bands for the survival curve of a specific subject. The methodology is extended to a multivariate version of a class of partly parametric additive hazards model. Simulation studies are conducted to assess finite sample properties, and the method is illustrated with an application to development of coronary heart diseases and cardiovascular accidents in the Framingham Heart Study. Copyright 2004, Oxford University Press.

Suggested Citation

  • Guosheng Yin & Jianwen Cai, 2004. "Additive hazards model with multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(4), pages 801-818, December.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:4:p:801-818
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    File URL: http://hdl.handle.net/10.1093/biomet/91.4.801
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    Citations

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    Cited by:

    1. Jie He & Hui Li & Shumei Zhang & Xiaogang Duan, 2019. "Additive hazards model with auxiliary subgroup survival information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 128-149, January.
    2. Adane F. Wogu & Haolin Li & Shanshan Zhao & Hazel B. Nichols & Jianwen Cai, 2023. "Additive subdistribution hazards regression for competing risks data in case‐cohort studies," Biometrics, The International Biometric Society, vol. 79(4), pages 3010-3022, December.
    3. 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.
    4. Giorgos Bakoyannis & Ying Zhang & Constantin T. Yiannoutsos, 2020. "Semiparametric regression and risk prediction with competing risks data under missing cause of failure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 659-684, October.
    5. Xiuyu J. Cong & Guosheng Yin & Yu Shen, 2007. "Marginal Analysis of Correlated Failure Time Data with Informative Cluster Sizes," Biometrics, The International Biometric Society, vol. 63(3), pages 663-672, September.
    6. 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.
    7. Ye, Peng & Zhao, Xingqiu & Sun, Liuquan & Xu, Wei, 2015. "A semiparametric additive rates model for multivariate recurrent events with missing event categories," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 39-50.
    8. David D. Hanagal, 2021. "RETRACTED ARTICLE: Positive Stable Shared Frailty Models Based on Additive Hazards," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 431-453, December.
    9. Ling Lan & Dipankar Bandyopadhyay & Somnath Datta, 2017. "Non-parametric regression in clustered multistate current status data with informative cluster size," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(1), pages 31-57, January.
    10. Goele Massonnet & Paul Janssen & Tomasz Burzykowski, 2008. "Fitting Conditional Survival Models to Meta‐Analytic Data by Using a Transformation Toward Mixed‐Effects Models," Biometrics, The International Biometric Society, vol. 64(3), pages 834-842, September.
    11. Hui Li & Xiaogang Duan & Guosheng Yin, 2016. "Generalized Method of Moments for Additive Hazards Model with Clustered Dental Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1124-1139, December.
    12. Ling Chen & Yanqin Feng & Jianguo Sun, 2017. "Regression analysis of clustered failure time data with informative cluster size under the additive transformation models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 651-670, October.
    13. Wang, Shuying & Wang, Chunjie & Wang, Peijie & Sun, Jianguo, 2018. "Semiparametric analysis of the additive hazards model with informatively interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 1-9.
    14. Teng Fei & John Hanfelt & Limin Peng, 2023. "Evaluating the association between latent classes and competing risks outcomes with multiphenotype data," Biometrics, The International Biometric Society, vol. 79(1), pages 488-501, March.
    15. 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.

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