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Marginal hazards model for multivariate failure time data with auxiliary covariates

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  • Zhaozhi Fan
  • Xiao-Feng Wang

Abstract

A marginal hazards model of multivariate failure times has been developed based on the ‘working independence’ assumption [L.J. Wei, D.Y. Lin, and L. Wessfeld, Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, J. Amer. Statist. Assoc. 84 (1989), pp. 1065–1073.]. In this article, we study the marginal hazards model of multivariate failure times with continuous auxiliary covariates. We consider the case of common baseline hazards for subjects from the same clusters. We extend the kernel smoothing procedure of Zhou and Wang [H. Zhou and C.Y. Wang, Failure time regression with continuous covariates measured with error, J. Roy. Statist. Soc. B 62 (2000), pp. 657–665.] to correlated failure time data. Through semiparametric estimation of the marginal partial likelihood function, we obtain the estimated partial likelihood based estimator of the regression coefficients. We present asymptotic properties of the induced estimator and demonstrate the performance of the proposed estimator through a finite sample simulation study. Finally, a real data application is conducted to elucidate the use of the method.

Suggested Citation

  • Zhaozhi Fan & Xiao-Feng Wang, 2009. "Marginal hazards model for multivariate failure time data with auxiliary covariates," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 771-786.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:7:p:771-786
    DOI: 10.1080/10485250902915903
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    References listed on IDEAS

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    1. Xiao Song & Yijian Huang, 2005. "On Corrected Score Approach for Proportional Hazards Model with Covariate Measurement Error," Biometrics, The International Biometric Society, vol. 61(3), pages 702-714, September.
    2. Halbo Zhou & C.‐Y. Wang, 2000. "Failure time regression with continuous covariates measured with error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 657-665.
    3. Wendy F. Greene & Jianwen Cai, 2004. "Measurement Error in Covariates in the Marginal Hazards Model for Multivariate Failure Time Data," Biometrics, The International Biometric Society, vol. 60(4), pages 987-996, December.
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    Cited by:

    1. Feifei Yan & Lin Zhu & Yanyan Liu & Jianwen Cai & Haibo Zhou, 2021. "Semiparametric regression based on quadratic inference function for multivariate failure time data with auxiliary information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 269-299, April.
    2. Xiaoping Shi & Yanyan Liu & Yuanshan Wu, 2014. "Auxiliary covariate in additive hazards regression for survival data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 101-113, March.
    3. Kaida Cai & Hua Shen & Xuewen Lu, 2022. "Adaptive bi-level variable selection for multivariate failure time model with a diverging number of covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 968-993, December.
    4. Feifei Yan & Yanyan Liu & Jianwen Cai & Haibo Zhou, 2023. "Estimated quadratic inference function for correlated failure time data," Biometrics, The International Biometric Society, vol. 79(2), pages 1145-1158, June.

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