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Additive–multiplicative hazards model with current status data

Author

Listed:
  • Wanrong Liu

    (Hunan Normal University)

  • Jianglin Fang

    (Hunan Normal University
    Hunan Institute of Engineering)

  • Xuewen Lu

    (Hunan Normal University
    University of Calgary)

Abstract

The additive–multiplicative hazards (AMH) regression model specifies an additive and multiplicative form on the hazard function for the counting process associated with a multidimensional covariate process, which contains the Cox proportional hazards model and the additive hazards model as its special cases. In this paper, we study the AMH model with current status data, where the cumulative hazard hazard function is assumed to be nonparametric and is estimated using B-splines with monotonicity constraint on the functional, while a simultaneous sieve maximum likelihood estimation is proposed to estimate regression parameters. The proposed estimator for the parameter vector is shown to be asymptotically normal and semiparametric efficient. The B-splines estimator of the functional of the cumulative hazard function is shown to achieve the optimal nonparametric rate of convergence. A simulation study is conducted to examine the finite sample performance of the proposed estimators and algorithm, and a real data example is presented for illustration.

Suggested Citation

  • Wanrong Liu & Jianglin Fang & Xuewen Lu, 2018. "Additive–multiplicative hazards model with current status data," Computational Statistics, Springer, vol. 33(3), pages 1245-1266, September.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:3:d:10.1007_s00180-018-0806-y
    DOI: 10.1007/s00180-018-0806-y
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    References listed on IDEAS

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    1. Minggen Lu & Ying Zhang & Jian Huang, 2007. "Estimation of the mean function with panel count data using monotone polynomial splines," Biometrika, Biometrika Trust, vol. 94(3), pages 705-718.
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    3. Debashis Ghosh, 2003. "Goodness-of-Fit Methods for Additive-Risk Models in Tumorigenicity Experiments," Biometrics, The International Biometric Society, vol. 59(3), pages 721-726, September.
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