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Semiparametric model for semi-competing risks data with application to breast cancer study

Author

Listed:
  • Renke Zhou

    (Baylor College of Medicine
    The University of Texas School of Public Health)

  • Hong Zhu

    (The University of Texas Southwestern Medical Center)

  • Melissa Bondy

    (Baylor College of Medicine)

  • Jing Ning

    (The University of Texas MD Anderson Cancer Center)

Abstract

For many forms of cancer, patients will receive the initial regimen of treatments, then experience cancer progression and eventually die of the disease. Understanding the disease process in patients with cancer is essential in clinical, epidemiological and translational research. One challenge in analyzing such data is that death dependently censors cancer progression (e.g., recurrence), whereas progression does not censor death. We deal with the informative censoring by first selecting a suitable copula model through an exploratory diagnostic approach and then developing an inference procedure to simultaneously estimate the marginal survival function of cancer relapse and an association parameter in the copula model. We show that the proposed estimators possess consistency and weak convergence. We use simulation studies to evaluate the finite sample performance of the proposed method, and illustrate it through an application to data from a study of early stage breast cancer.

Suggested Citation

  • Renke Zhou & Hong Zhu & Melissa Bondy & Jing Ning, 2016. "Semiparametric model for semi-competing risks data with application to breast cancer study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 456-471, July.
    • Handle: RePEc:spr:lifeda:v:22:y:2016:i:3:d:10.1007_s10985-015-9344-x
    DOI: 10.1007/s10985-015-9344-x
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    References listed on IDEAS

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    1. Weijing Wang, 2003. "Estimating the association parameter for copula models under dependent censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 257-273, February.
    2. A. Adam Ding & Guangkai Shi & Weijing Wang & Jin‐Jian Hsieh, 2009. "Marginal Regression Analysis for Semi‐Competing Risks Data Under Dependent Censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 481-500, September.
    3. 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.
    4. R.D. Gill, 1980. "Censoring and Stochastic Integrals," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 34(2), pages 124-124, June.
    5. Jinfeng Xu & John D. Kalbfleisch & Beechoo Tai, 2010. "Statistical Analysis of Illness–Death Processes and Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 66(3), pages 716-725, September.
    6. Hongyu Jiang & Jason P. Fine & Michael R. Kosorok & Rick Chappell, 2005. "Pseudo Self‐Consistent Estimation of a Copula Model with Informative Censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(1), pages 1-20, March.
    7. 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:

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    2. Jin Piao & Jing Ning & Yu Shen, 2019. "Semiparametric model for bivariate survival data subject to biased sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 409-429, April.

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