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Semiparametric regression based on quadratic inference function for multivariate failure time data with auxiliary information

Author

Listed:
  • Feifei Yan

    (Huazhong University of Science and Technology
    East China University of Technology)

  • Lin Zhu

    (East China University of Technology)

  • Yanyan Liu

    (Wuhan University)

  • Jianwen Cai

    (University of North Carolina at Chapel Hill)

  • Haibo Zhou

    (University of North Carolina at Chapel Hill)

Abstract

This paper deals with statistical inference procedure of multivariate failure time data when the primary covariate can be measured only on a subset of the full cohort but the auxiliary information is available. To improve efficiency of statistical inference, we use quadratic inference function approach to incorporate the intra-cluster correlation and use kernel smoothing technique to further utilize the auxiliary information. The proposed method is shown to be more efficient than those ignoring the intra-cluster correlation and auxiliary information and is easy to implement. In addition, we develop a chi-squared test for hypothesis testing of hazard ratio parameters. We evaluate the finite-sample performance of the proposed procedure via extensive simulation studies. The proposed approach is illustrated by analysis of a real data set from the study of left ventricular dysfunction.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:lifeda:v:27:y:2021:i:2:d:10.1007_s10985-020-09513-1
    DOI: 10.1007/s10985-020-09513-1
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    References listed on IDEAS

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    1. Yanyan Liu & Haibo Zhou & Jianwen Cai, 2009. "Estimated Pseudopartial-Likelihood Method for Correlated Failure Time Data with Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1184-1193, December.
    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. Liu, Yanyan & Wu, Yuanshan & Zhou, Haibo, 2010. "Multivariate failure times regression with a continuous auxiliary covariate," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 679-691, March.
    4. 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.
    5. Hu, Chengcheng & Lin, D.Y., 2004. "Semiparametric Failure Time Regression With Replicates of Mismeasured Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 105-118, January.
    6. 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.
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    Cited by:

    1. Hongkai Liang & Xiaoguang Wang & Yingwei Peng & Yi Niu, 2023. "Improving marginal hazard ratio estimation using quadratic inference functions," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 823-853, October.

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