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Regression analysis of additive hazards model with sparse longitudinal covariates

Author

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  • Zhuowei Sun

    (Jilin University)

  • Hongyuan Cao

    (Jilin University
    Florida State University)

  • Li Chen

    (East Hanover)

Abstract

Additive hazards model is often used to complement the proportional hazards model in the analysis of failure time data. Statistical inference of additive hazards model with time-dependent longitudinal covariates requires the availability of the whole trajectory of the longitudinal process, which is not realistic in practice. The commonly used last value carried forward approach for intermittently observed longitudinal covariates can induce biased parameter estimation. The more principled joint modeling of the longitudinal process and failure time data imposes strong modeling assumptions, which is difficult to verify. In this paper, we propose methods that weigh the distance between the observational time of longitudinal covariates and the failure time, resulting in unbiased regression coefficient estimation. We establish the consistency and asymptotic normality of the proposed estimators. Simulation studies provide numerical support for the theoretical findings. Data from an Alzheimer’s study illustrate the practical utility of the methodology.

Suggested Citation

  • Zhuowei Sun & Hongyuan Cao & Li Chen, 2022. "Regression analysis of additive hazards model with sparse longitudinal covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 263-281, April.
    • Handle: RePEc:spr:lifeda:v:28:y:2022:i:2:d:10.1007_s10985-022-09548-6
    DOI: 10.1007/s10985-022-09548-6
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    References listed on IDEAS

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    1. Hongyuan Cao & Mathew M. Churpek & Donglin Zeng & Jason P. Fine, 2015. "Analysis of the Proportional Hazards Model With Sparse Longitudinal Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1187-1196, September.
    2. Torben Martinussen, 2002. "A flexible additive multiplicative hazard model," Biometrika, Biometrika Trust, vol. 89(2), pages 283-298, June.
    3. Rizopoulos, Dimitris, 2010. "JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i09).
    4. Jiancheng Jiang & Zhou Haibo, 2007. "Additive hazard regression with auxiliary covariates," Biometrika, Biometrika Trust, vol. 94(2), pages 359-369.
    5. Hongyuan Cao & Donglin Zeng & Jason P. Fine, 2015. "Regression analysis of sparse asynchronous longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(4), pages 755-776, September.
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    Cited by:

    1. Ting Li & Huichen Zhu & Tengfei Li & Hongtu Zhu, 2023. "Asynchronous functional linear regression models for longitudinal data in reproducing kernel Hilbert space," Biometrics, The International Biometric Society, vol. 79(3), pages 1880-1895, September.

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