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Composite partial likelihood estimation for length-biased and right-censored data with competing risks

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  • Zhang, Feipeng
  • Peng, Heng
  • Zhou, Yong

Abstract

This paper considers a competing risks model for survival data from length-biased sampling, where the survival times are left truncated by uniformly distributed random truncation times. We propose a composite partial likelihood estimating procedure for cause-specific failure probabilities using competing risks data. We establish the asymptotic properties of the proposed estimators, and present predictions of the cumulative incidence functions. Furthermore, we show how to construct simultaneous confidence bands for the cause-specific cumulative incidence functions for subjects with given risk factors. A simulation study demonstrates that the proposed estimators have good finite-sample performance. A real data example illustrates the method and the theory.

Suggested Citation

  • Zhang, Feipeng & Peng, Heng & Zhou, Yong, 2016. "Composite partial likelihood estimation for length-biased and right-censored data with competing risks," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 160-176.
    • Handle: RePEc:eee:jmvana:v:149:y:2016:i:c:p:160-176
    DOI: 10.1016/j.jmva.2016.04.002
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    References listed on IDEAS

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    1. Debashis Ghosh, 2008. "Proportional Hazards Regression for Cancer Studies," Biometrics, The International Biometric Society, vol. 64(1), pages 141-148, March.
    2. Chiung-yu Huang & Jing Qin, 2012. "Composite Partial Likelihood Estimation Under Length-Biased Sampling, With Application to a Prevalent Cohort Study of Dementia," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 946-957, September.
    3. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    4. Jing Qin & Yu Shen, 2010. "Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model," Biometrics, The International Biometric Society, vol. 66(2), pages 382-392, June.
    5. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504.
    6. Xiaodong Luo & Wei Yann Tsai, 2009. "Nonparametric estimation for right-censored length-biased data: a pseudo-partial likelihood approach," Biometrika, Biometrika Trust, vol. 96(4), pages 873-886.
    7. Chiung-Yu Huang & Jing Qin, 2011. "Nonparametric estimation for length-biased and right-censored data," Biometrika, Biometrika Trust, vol. 98(1), pages 177-186.
    8. Chiung-Yu Huang & Jing Qin & Dean A. Follmann, 2012. "A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling," Biometrika, Biometrika Trust, vol. 99(1), pages 199-210.
    9. Lancaster,Tony, 1992. "The Econometric Analysis of Transition Data," Cambridge Books, Cambridge University Press, number 9780521437899.
    10. Ronald B. Geskus, 2011. "Cause-Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring," Biometrics, The International Biometric Society, vol. 67(1), pages 39-49, March.
    11. Wei Yann Tsai, 2009. "Pseudo-partial likelihood for proportional hazards models with biased-sampling data," Biometrika, Biometrika Trust, vol. 96(3), pages 601-615.
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