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Model checking for additive hazards model with multivariate survival data

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  • Yin, Guosheng

Abstract

Multivariate failure time data often arise in biomedical studies due to natural or artificial clustering. With appropriate adjustment for the underlying correlation, the marginal additive hazards model characterizes the hazard difference via a linear link function between the hazard and covariates. We propose a class of graphical and numerical methods to assess the overall fitting adequacy of the marginal additive hazards model. The test statistics are based on the supremum of the stochastic processes derived from the cumulative sum of the martingale-based residuals over time and/or covariates. The distribution of the stochastic process can be approximated through a simulation technique. The proposed tests examine how unusual the observed stochastic process is, compared to a large number of realizations from the approximated process. This class of tests is very general and suitable for various purposes of model fitting evaluation. Simulation studies are conducted to examine the finite sample performance, and the model-checking methods are illustrated with data from an otitis media study.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jmvana:v:98:y:2007:i:5:p:1018-1032
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    References listed on IDEAS

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    1. Yu Shen & S. C. Cheng, 1999. "Confidence Bands for Cumulative Incidence Curves Under the Additive Risk Model," Biometrics, The International Biometric Society, vol. 55(4), pages 1093-1100, December.
    2. Paul S. F. Yip & Yong Zhou & D. Y. Lin & Xiang-Zhong Fang, 1999. "Estimation of Population Size Based on Additive Hazards Models for Continuous-Time Recapture Experiments," Biometrics, The International Biometric Society, vol. 55(3), pages 904-908, September.
    3. Guosheng Yin & Jianwen Cai, 2004. "Additive hazards model with multivariate failure time data," Biometrika, Biometrika Trust, vol. 91(4), pages 801-818, December.
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    Cited by:

    1. 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.

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