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Censored quantile regression with partially functional effects

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  • Jing Qian
  • Limin Peng

Abstract

Quantile regression offers a flexible approach to analyzing survival data, allowing each covariate effect to vary with quantiles. In practice, constancy is often found to be adequate for some covariates. In this paper, we study censored quantile regression tailored to the partially functional effect setting with a mixture of varying and constant effects. Such a model can offer a simpler view regarding covariate-survival association and, moreover, can enable improvement in estimation efficiency. We propose profile estimating equations and present an iterative algorithm that can be readily and stably implemented. Asymptotic properties of the resultant estimators are established. A simple resampling-based inference procedure is developed and justified. Extensive simulation studies demonstrate efficiency gains of the proposed method over a naive two-stage procedure. The proposed method is illustrated via an application to a recent renal dialysis study. Copyright 2010, Oxford University Press.

Suggested Citation

  • Jing Qian & Limin Peng, 2010. "Censored quantile regression with partially functional effects," Biometrika, Biometrika Trust, vol. 97(4), pages 839-850.
    • Handle: RePEc:oup:biomet:v:97:y:2010:i:4:p:839-850
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    File URL: http://hdl.handle.net/10.1093/biomet/asq050
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    Citations

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    Cited by:

    1. Xie, Shangyu & Wan, Alan T.K. & Zhou, Yong, 2015. "Quantile regression methods with varying-coefficient models for censored data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 154-172.
    2. Jiang Du & Zhongzhan Zhang & Tianfa Xie, 2017. "Focused information criterion and model averaging in censored quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(5), pages 547-570, July.
    3. J. E. Soh & Yijian Huang, 2021. "A varying-coefficient model for gap times between recurrent events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 437-459, July.
    4. Yijian Huang, 2017. "Restoration of Monotonicity Respecting in Dynamic Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 613-622, April.
    5. Tang, Yanlin & Wang, Huixia Judy, 2015. "Penalized regression across multiple quantiles under random censoring," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 132-146.
    6. Li, Ruosha & Peng, Limin, 2014. "Varying coefficient subdistribution regression for left-truncated semi-competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 65-78.

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