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Sieve maximum likelihood estimation for the proportional hazards model under informative censoring

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  • Chen, Xuerong
  • Hu, Tao
  • Sun, Jianguo

Abstract

Failure time data often occur in many areas such as clinical trails, economics and medical follow-up studies, and a great deal of literature has been developed for their analysis when the censoring is noninformative. A number of methods have also been developed for the situation where the censoring may be informative. However, most of the existing procedures for the latter case apply only to limited situations or may not be stable or robust. In this paper, we present a copula model approach for regression analysis of right-censored failure time data in the presence of informative censoring. In the method, the copula model is used to describe the dependence between the failure time of interest and censoring time and for estimation, a sieve maximum likelihood estimation procedure is developed. In addition, the asymptotic properties of the proposed estimators are established and the simulation study indicates that the proposed method seems to work well in practice. An illustrative example is also provided.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:112:y:2017:i:c:p:224-234
    DOI: 10.1016/j.csda.2017.03.006
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    References listed on IDEAS

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    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. Chen, Xiaohong & Fan, Yanqin & Tsyrennikov, Viktor, 2006. "Efficient Estimation of Semiparametric Multivariate Copula Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1228-1240, September.
    3. Minggen Lu & Ying Zhang & Jian Huang, 2007. "Estimation of the mean function with panel count data using monotone polynomial splines," Biometrika, Biometrika Trust, vol. 94(3), pages 705-718.
    4. 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.
    5. Jin‐Jian Hsieh & Weijing Wang & A. Adam Ding, 2008. "Regression analysis based on semicompeting risks data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 3-20, February.
    6. Lu, Zudi & Zhang, Wenyang, 2012. "Semiparametric likelihood estimation in survival models with informative censoring," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 187-211.
    7. 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.
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    Cited by:

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    2. Qingzhi Zhong & Huazhen Lin & Yi Li, 2021. "Cluster non‐Gaussian functional data," Biometrics, The International Biometric Society, vol. 77(3), pages 852-865, September.

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