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Sieve maximum likelihood regression analysis of dependent current status data

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  • Ling Ma
  • Tao Hu
  • Jianguo Sun

Abstract

Current status data occur in contexts including demographic studies and tumorigenicity experiments. In such cases, each subject is observed only once and the failure time of interest is either left- or right-censored (Kalbfleisch & Prentice, 2002). Many methods have been developed for the analysis of such data (Huang, 1996; Sun, 2006), most of which assume that the failure time and the observation time are independent completely or given covariates. In this paper, we present a sieve maximum likelihood approach for current status data when independence does not hold. A copula model and monotone I-splines are used and the asymptotic properties of the resulting estimators are established. In particular, the estimated regression parameters are shown to be semiparametrically efficient. An illustrative example is provided.

Suggested Citation

  • Ling Ma & Tao Hu & Jianguo Sun, 2015. "Sieve maximum likelihood regression analysis of dependent current status data," Biometrika, Biometrika Trust, vol. 102(3), pages 731-738.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:3:p:731-738.
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    File URL: http://hdl.handle.net/10.1093/biomet/asv020
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    Cited by:

    1. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2018. "Semiparametric regression analysis of interval‐censored data with informative dropout," Biometrics, The International Biometric Society, vol. 74(4), pages 1213-1222, December.
    2. 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.
    3. Mengyue Zhang & Shishun Zhao & Tao Hu & Da Xu & Jianguo Sun, 2023. "Regression Analysis of Dependent Current Status Data with Left Truncation," Mathematics, MDPI, vol. 11(16), pages 1-13, August.
    4. 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.
    5. Huazhen Yu & Rui Zhang & Lixin Zhang, 2024. "Copula-based analysis of dependent current status data with semiparametric linear transformation model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(4), pages 742-775, October.
    6. Qingzhi Zhong & Huazhen Lin & Yi Li, 2021. "Cluster non‐Gaussian functional data," Biometrics, The International Biometric Society, vol. 77(3), pages 852-865, September.
    7. 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.
    8. Da Xu & Hui Zhao & Jianguo Sun, 2018. "Joint analysis of interval-censored failure time data and panel count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 94-109, January.
    9. Du, Mingyue & Zhao, Xingqiu, 2024. "A conditional approach for regression analysis of case K interval-censored failure time data with informative censoring," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
    10. 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.
    11. Peijie Wang & Hui Zhao & Jianguo Sun, 2016. "Regression analysis of case K interval‐censored failure time data in the presence of informative censoring," Biometrics, The International Biometric Society, vol. 72(4), pages 1103-1112, December.
    12. 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).
    13. Liu, Xiaoyu & Xiang, Liming, 2021. "Generalized accelerated hazards mixture cure models with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    14. Wang, Shuying & Wang, Chunjie & Wang, Peijie & Sun, Jianguo, 2018. "Semiparametric analysis of the additive hazards model with informatively interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 1-9.
    15. 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.
    16. Chen, Xuerong & Hu, Tao & Sun, Jianguo, 2017. "Sieve maximum likelihood estimation for the proportional hazards model under informative censoring," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 224-234.
    17. An-Min Tang & Nian-Sheng Tang & Dalei Yu, 2023. "Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 888-918, October.
    18. Liu, Wenting & Li, Huiqiong & Tang, Niansheng & Lyu, Jun, 2024. "Variational Bayesian approach for analyzing interval-censored data under the proportional hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 195(C).
    19. 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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