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Inference for the treatment effects in two sample problems with right-censored and length-biased data

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  • Lin, Cunjie
  • Zhou, Yong

Abstract

In the study of comparing treatment effects, the data structures of two samples may be different. In this paper, we develop a unified semiparametric estimating equation approach to estimate various types of treatment effects with right-censored and length-biased data based on a semiparametric two-sample model. The large sample properties of the proposed estimators are derived and numerical studies are conducted to illustrate the proposed methods.

Suggested Citation

  • Lin, Cunjie & Zhou, Yong, 2014. "Inference for the treatment effects in two sample problems with right-censored and length-biased data," Statistics & Probability Letters, Elsevier, vol. 90(C), pages 17-24.
    • Handle: RePEc:eee:stapro:v:90:y:2014:i:c:p:17-24
    DOI: 10.1016/j.spl.2014.03.009
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    References listed on IDEAS

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    1. Asgharian M. & MLan C.E. & Wolfson D. B., 2002. "Length-Biased Sampling With Right Censoring: An Unconditional Approach," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 201-209, March.
    2. Yong Zhou & Hua Liang, 2005. "Empirical-likelihood-based semiparametric inference for the treatment effect in the two-sample problem with censoring," Biometrika, Biometrika Trust, vol. 92(2), pages 271-282, June.
    3. Jing Qin & Yu Shen, 2010. "Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model," Biometrics, The International Biometric Society, vol. 66(2), pages 382-392, June.
    4. Qin, Yong Song, 1997. "Semi-parametric likelihood ratio confidence intervals for various differences of two populations," Statistics & Probability Letters, Elsevier, vol. 33(2), pages 135-143, April.
    5. Shen, Yu & Ning, Jing & Qin, Jing, 2009. "Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1192-1202.
    6. 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).
    2. 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.
    3. Li Xun & Li Tao & Yong Zhou, 2020. "Estimators of quantile difference between two samples with length-biased and right-censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 409-429, June.

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