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A nonparametric test for current status data with unequal censoring

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  • J. Sun

Abstract

Nonparametric tests for the comparison of different treatments based on current status data are proposed. For this problem, most methods proposed in the literature require that observation times on all subjects follow the same distribution. In other words, censoring distributions are identical between the treatment groups. In this paper, we focus on the situation where the censoring distributions may be different for subjects in different treatment groups and the test that can take this unequal censoring into account is given. The asymptotic distribution of the test proposed is derived. The method proposed is applied to data arising from a tumorigenicity experiment.

Suggested Citation

  • J. Sun, 1999. "A nonparametric test for current status data with unequal censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 243-250.
    • Handle: RePEc:bla:jorssb:v:61:y:1999:i:1:p:243-250
    DOI: 10.1111/1467-9868.00174
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    Cited by:

    1. Tianyi Lu & Shuwei Li & Liuquan Sun, 2023. "Combined estimating equation approaches for the additive hazards model with left-truncated and interval-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 672-697, July.
    2. Mengzhu Yu & Mingyue Du, 2022. "Regression Analysis of Multivariate Interval-Censored Failure Time Data under Transformation Model with Informative Censoring," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    3. Chunjie Wang & Bo Zhao & Linlin Luo & Xinyuan Song, 2021. "Regression analysis of current status data with latent variables," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(3), pages 413-436, July.
    4. Yanqin Feng & Yurong Chen, 2018. "Regression analysis of current status data with auxiliary covariates and informative observation times," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 293-309, April.
    5. Zhao, Xingqiu & Duan, Ran & Zhao, Qiang & Sun, Jianguo, 2013. "A new class of generalized log rank tests for interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 123-131.

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