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Regression Survival Analysis with an Assumed Copula for Dependent Censoring: A Sensitivity Analysis Approach

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  • Xuelin Huang
  • Nan Zhang

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  • Xuelin Huang & Nan Zhang, 2008. "Regression Survival Analysis with an Assumed Copula for Dependent Censoring: A Sensitivity Analysis Approach," Biometrics, The International Biometric Society, vol. 64(4), pages 1090-1099, December.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:4:p:1090-1099
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.00986.x
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    References listed on IDEAS

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    1. Daniel O. Scharfstein, 2002. "Estimation of the failure time distribution in the presence of informative censoring," Biometrika, Biometrika Trust, vol. 89(3), pages 617-634, August.
    2. Xuelin Huang & Robert A. Wolfe, 2002. "A Frailty Model for Informative Censoring," Biometrics, The International Biometric Society, vol. 58(3), pages 510-520, September.
    3. Daniel Scharfstein & James M. Robins & Wesley Eddings & Andrea Rotnitzky, 2001. "Inference in Randomized Studies with Informative Censoring and Discrete Time-to-Event Endpoints," Biometrics, The International Biometric Society, vol. 57(2), pages 404-413, June.
    4. Jiameng Zhang & Daniel F. Heitjan, 2006. "A Simple Local Sensitivity Analysis Tool for Nonignorable Coarsening: Application to Dependent Censoring," Biometrics, The International Biometric Society, vol. 62(4), pages 1260-1268, December.
    5. Fotios Siannis, 2004. "Applications of a Parametric Model for Informative Censoring," Biometrics, The International Biometric Society, vol. 60(3), pages 704-714, September.
    6. Limin Peng & Jason P. Fine, 2007. "Regression Modeling of Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 63(1), pages 96-108, March.
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    Cited by:

    1. Yi‐Hau Chen, 2010. "Semiparametric marginal regression analysis for dependent competing risks under an assumed copula," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 235-251, March.
    2. Cuihong Zhang & Jing Ning & Steven H. Belle & Robert H. Squires & Jianwen Cai & Ruosha Li, 2022. "Assessing predictive discrimination performance of biomarkers in the presence of treatment‐induced dependent censoring," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1137-1157, November.
    3. Kim, Dongwoo, 2023. "Partially identifying competing risks models: An application to the war on cancer," Journal of Econometrics, Elsevier, vol. 234(2), pages 536-564.
    4. Deresa, N.W. & Van Keilegom, I. & Antonio, K., 2022. "Copula-based inference for bivariate survival data with left truncation and dependent censoring," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 1-21.
    5. Luciana Dalla Valle & Fabrizio Leisen & Luca Rossini, 2018. "Bayesian non‐parametric conditional copula estimation of twin data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 523-548, April.
    6. 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.
    7. 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.
    8. Woraphon Yamaka & Paravee Maneejuk & Rungrapee Phadkantha & Wiranya Puntoon & Payap Tarkhamtham & Tatcha Sudtasan, 2023. "Survival and Duration Analysis of MSMEs in Chiang Mai, Thailand: Evidence from the Post-COVID-19 Recovery," Mathematics, MDPI, vol. 11(4), pages 1-21, February.
    9. Deresa, Negera Wakgari & Van Keilegom, Ingrid, 2020. "A multivariate normal regression model for survival data subject to different types of dependent censoring," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    10. Sangbum Choi & Xuelin Huang, 2014. "Maximum likelihood estimation of semiparametric mixture component models for competing risks data," Biometrics, The International Biometric Society, vol. 70(3), pages 588-598, September.
    11. Chia-Hui Huang, 2019. "Mixture regression models for the gap time distributions and illness–death processes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 168-188, January.

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