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Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure

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  • Guozhi Gao
  • Anastasios A. Tsiatis

Abstract

We consider the problem of estimating the regression coefficients in a competing risks model, where the relationship between the cause-specific hazard for the cause of interest and covariates is described using linear transformation models and when cause of failure is missing at random for a subset of individuals. Using the theory of Robins et al. (1994) for missing data problems and the approach of Chen et al. (2002) for estimating regression coefficients for linear transformation models, we derive augmented inverse probability weighted complete-case estimators for the regression coefficients that are doubly robust. Simulations demonstrate the relevance of the theory in finite samples. Copyright 2005, Oxford University Press.

Suggested Citation

  • Guozhi Gao & Anastasios A. Tsiatis, 2005. "Semiparametric estimators for the regression coefficients in the linear transformation competing risks model with missing cause of failure," Biometrika, Biometrika Trust, vol. 92(4), pages 875-891, December.
    • Handle: RePEc:oup:biomet:v:92:y:2005:i:4:p:875-891
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    File URL: http://hdl.handle.net/10.1093/biomet/92.4.875
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    Cited by:

    1. Wang, Qihua & Liu, Wei & Liu, Chunling, 2009. "Probability density estimation for survival data with censoring indicators missing at random," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 835-850, May.
    2. Yanqing Sun & Li Qi & Fei Heng & Peter B. Gilbert, 2020. "A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 791-814, August.
    3. Giorgos Bakoyannis & Ying Zhang & Constantin T. Yiannoutsos, 2020. "Semiparametric regression and risk prediction with competing risks data under missing cause of failure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 659-684, October.
    4. Yanqing Sun & Xiyuan Qian & Qiong Shou & Peter B. Gilbert, 2017. "Analysis of two-phase sampling data with semiparametric additive hazards models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 377-399, July.
    5. Gu, Minggao & Wu, Yueqin & Huang, Bin, 2014. "Partial marginal likelihood estimation for general transformation models," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 1-18.
    6. Natalia A. Gouskova & Feng-Chang Lin & Jason P. Fine, 2017. "Nonparametric analysis of competing risks data with event category missing at random," Biometrics, The International Biometric Society, vol. 73(1), pages 104-113, March.
    7. Fei Heng & Yanqing Sun & Seunggeun Hyun & Peter B. Gilbert, 2020. "Analysis of the time-varying Cox model for the cause-specific hazard functions with missing causes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 731-760, October.
    8. Qiu, Zhiping & Chen, Xiaoping & Zhou, Yong, 2015. "A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 89-97.
    9. Daniel Nevo & Reiko Nishihara & Shuji Ogino & Molin Wang, 2018. "The competing risks Cox model with auxiliary case covariates under weaker missing-at-random cause of failure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 425-442, July.
    10. Qihua Wang & Gregg Dinse & Chunling Liu, 2012. "Hazard function estimation with cause-of-death data missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 415-438, April.
    11. Teng Fei & John Hanfelt & Limin Peng, 2023. "Evaluating the association between latent classes and competing risks outcomes with multiphenotype data," Biometrics, The International Biometric Society, vol. 79(1), pages 488-501, March.
    12. Samiran Sinha & Yanyuan Ma, 2014. "Semiparametric analysis of linear transformation models with covariate measurement errors," Biometrics, The International Biometric Society, vol. 70(1), pages 21-32, March.

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