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Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments

Author

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  • Ying Yan

    (University of Waterloo)

  • Grace Y. Yi

    (University of Waterloo)

Abstract

Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.

Suggested Citation

  • Ying Yan & Grace Y. Yi, 2016. "Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 321-342, July.
    • Handle: RePEc:spr:lifeda:v:22:y:2016:i:3:d:10.1007_s10985-015-9340-1
    DOI: 10.1007/s10985-015-9340-1
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    References listed on IDEAS

    as
    1. Jiancheng Jiang & Zhou Haibo, 2007. "Additive hazard regression with auxiliary covariates," Biometrika, Biometrika Trust, vol. 94(2), pages 359-369.
    2. 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.
    3. Liuquan Sun & Zhigang Zhang & Jianguo Sun, 2006. "Additive hazards regression of failure time data with covariate measurement errors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(4), pages 497-509, November.
    4. Li Y. & Lin X., 2003. "Functional Inference in Frailty Measurement Error Models for Clustered Survival Data Using the SIMEX Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 191-203, January.
    5. Yi Li & Louise Ryan, 2004. "Survival Analysis With Heterogeneous Covariate Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 724-735, January.
    6. 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.
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    Cited by:

    1. Xiaobo Wang & Jiayu Huang & Guosheng Yin & Jian Huang & Yuanshan Wu, 2023. "Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 115-141, January.

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