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Regression analysis of current status data in the presence of a cured subgroup and dependent censoring

Author

Listed:
  • Yeqian Liu

    (Middle Tennessee State University)

  • Tao Hu

    (Capital Normal University)

  • Jianguo Sun

    (University of Missouri)

Abstract

This paper discusses regression analysis of current status data, a type of failure time data where each study subject is observed only once, in the presence of dependent censoring. Furthermore, there may exist a cured subgroup, meaning that a proportion of study subjects are not susceptible to the failure event of interest. For the problem, we develop a sieve maximum likelihood estimation approach with the use of latent variables and Bernstein polynomials. For the determination of the proposed estimators, an EM algorithm is developed and the asymptotic properties of the estimators are established. Extensive simulation studies are conducted and indicate that the proposed method works well for practical situations. A motivating application from a tumorigenicity experiment is also provided.

Suggested Citation

  • Yeqian Liu & Tao Hu & Jianguo Sun, 2017. "Regression analysis of current status data in the presence of a cured subgroup and dependent censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 626-650, October.
    • Handle: RePEc:spr:lifeda:v:23:y:2017:i:4:d:10.1007_s10985-016-9382-z
    DOI: 10.1007/s10985-016-9382-z
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    References listed on IDEAS

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    Cited by:

    1. Frederico Machado Almeida & Enrico Antônio Colosimo & Vinícius Diniz Mayrink, 2021. "Firth adjusted score function for monotone likelihood in the mixture cure fraction model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 131-155, January.
    2. Richard Tawiah & Geoffrey J. McLachlan & Shu Kay Ng, 2020. "A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction," Biometrics, The International Biometric Society, vol. 76(3), pages 753-766, September.
    3. Xiaoguang Wang & Ziwen Wang, 2021. "EM algorithm for the additive risk mixture cure model with interval-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 91-130, January.

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