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Mean residual life models with time-dependent coefficients under right censoring

Author

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  • Liuquan Sun
  • Xinyuan Song
  • Zhigang Zhang

Abstract

The mean residual life provides the remaining life expectancy of a subject who has survived to a certain time-point. When covariates are present, regression models are needed to study the association between the mean residual life function and potential regression covariates. In this paper, we propose a flexible class of semiparametric mean residual life models where some effects may be time-varying and some may be constant over time. In the presence of right censoring, we use the inverse probability of censoring weighting approach and develop inference procedures for estimating the model parameters. In addition, we provide graphical and numerical methods for model checking and tests for examining whether or not the covariate effects vary with time. Asymptotic and finite sample properties of the proposed estimators are established and the approach is applied to real life datasets collected from clinical trials. Copyright 2012, Oxford University Press.

Suggested Citation

  • Liuquan Sun & Xinyuan Song & Zhigang Zhang, 2012. "Mean residual life models with time-dependent coefficients under right censoring," Biometrika, Biometrika Trust, vol. 99(1), pages 185-197.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:1:p:185-197
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    File URL: http://hdl.handle.net/10.1093/biomet/asr065
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    Citations

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    Cited by:

    1. Peng Jin & Anne Zeleniuch-Jacquotte & Mengling Liu, 2020. "Generalized mean residual life models for case-cohort and nested case-control studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 789-819, October.
    2. Zahra Mansourvar & Torben Martinussen & Thomas H. Scheike, 2016. "An Additive–Multiplicative Restricted Mean Residual Life Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 487-504, June.
    3. Xiaofeng Lv & Gupeng Zhang & Guangyu Ren, 2017. "Gini index estimation for lifetime data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 275-304, April.
    4. Yixin Wang & Ying Qing Chen, 2019. "Estimating Attributable Life Expectancy Under the Proportional Mean Residual Life Model," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 659-676, December.
    5. Kyu Hyun Kim & Daniel J. Caplan & Sangwook Kang, 2023. "Smoothed quantile regression for censored residual life," Computational Statistics, Springer, vol. 38(2), pages 1001-1022, June.
    6. Wu, Hongping & Cao, Xiaomin & Du, Caifeng, 2019. "Estimating equations of additive mean residual life model with censored length-biased data," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    7. Ruosha Li & Xuelin Huang & Jorge Cortes, 2016. "Quantile residual life regression with longitudinal biomarker measurements for dynamic prediction," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 755-773, November.
    8. Peng Liu & Yixin Wang & Yong Zhou, 2015. "Quantile residual lifetime with right-censored and length-biased data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(5), pages 999-1028, October.
    9. Ruiwen Zhou & Jianguo Sun, 2022. "Estimation of the Proportional Mean Residual Life Model with Internal and Longitudinal Covariates," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 550-563, December.
    10. Yang, Guangren & Zhou, Yong, 2014. "Semiparametric varying-coefficient study of mean residual life models," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 226-238.

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