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Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling

Author

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  • Yifan He

    (Shanghai University of Finance and Economics)

  • Yong Zhou

    (East China Normal University)

Abstract

Restricted mean survival time is often of direct interest in epidemiologic studies involving censored survival time. In this article, we propose the nonparametric and semiparametric estimators of the mean restricted to the preassigned interval with censored length-biased data. Based on the peculiarity of length-biased data, the auxiliary information that truncation time and residual time have the same distribution is taken into account for improving estimation efficiency. For two-sample comparison, we construct two tests which are easy to implement. We also derive the asymptotic properties for the proposed estimators and test statistics. In simulation studies, some simulations are conducted to compare the performances of several approaches to estimate restricted mean and to assess the test statistics. In addition, our methods are applied to a real data example and some interesting results are presented.

Suggested Citation

  • Yifan He & Yong Zhou, 2020. "Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 761-788, October.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:4:d:10.1007_s10985-020-09498-x
    DOI: 10.1007/s10985-020-09498-x
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    References listed on IDEAS

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    9. Wolkewitz, Martin & Allignol, Arthur & Schumacher, Martin & Beyersmann, Jan, 2010. "Two Pitfalls in Survival Analyses of Time-Dependent Exposure: A Case Study in a Cohort of Oscar Nominees," The American Statistician, American Statistical Association, vol. 64(3), pages 205-211.
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    Cited by:

    1. Yang, Xiaoran & Du, Junjie & Bai, Fangfang, 2023. "Semiparametric inference of treatment effects on restricted mean survival time in two sample problems from length-biased samples," Statistics & Probability Letters, Elsevier, vol. 193(C).

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