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Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments

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  • Li, Shuwei
  • Hu, Tao
  • Wang, Peijie
  • Sun, Jianguo

Abstract

Current status data frequently occur in many fields including demographic studies and tumorigenicity experiments. In these cases, the censoring or observation time may be correlated to the failure time of interest, the situation that is often referred to as dependent or informative censoring. Although several semiparametric methods have been developed in the literature for the situation, they either only apply to limited situations or may be computationally unstable. To address these, a frailty model-based maximum likelihood approach is proposed with the use of monotone splines to approximate the unknown baseline cumulative hazard function of the failure time. Also a novel EM algorithm, which is based on a three-stage data augmentation and can be easily implemented, is presented. The proposed estimators are proved to be consistent and asymptotically normally distributed. An extensive simulation study is performed to assess the finite sample performance of the proposed approach and suggests that it works well for practical situations. An application to a tumorigenicity study is provided.

Suggested Citation

  • Li, Shuwei & Hu, Tao & Wang, Peijie & Sun, Jianguo, 2017. "Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 75-86.
    • Handle: RePEc:eee:csdana:v:110:y:2017:i:c:p:75-86
    DOI: 10.1016/j.csda.2016.12.011
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Shuying & Wang, Chunjie & Wang, Peijie & Sun, Jianguo, 2020. "Estimation of the additive hazards model with case K interval-censored failure time data in the presence of informative censoring," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    3. Ruiwen Zhou & Huiqiong Li & Jianguo Sun & Niansheng Tang, 2022. "A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 335-355, July.
    4. Shuying Wang & Chunjie Wang & Jianguo Sun, 2021. "An additive hazards cure model with informative interval censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 244-268, April.

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