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Marginal Regression Analysis for Semi‐Competing Risks Data Under Dependent Censoring

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  • A. ADAM DING
  • GUANGKAI SHI
  • WEIJING WANG
  • JIN‐JIAN HSIEH

Abstract

. Multiple events data are commonly seen in medical applications. There are two types of events, namely terminal and non‐terminal. Statistical analysis for non‐terminal events is complicated due to dependent censoring. Consequently, joint modelling and inference are often needed to avoid the problem of non‐identifiability. This article considers regression analysis for multiple events data with major interest in a non‐terminal event such as disease progression. We generalize the technique of artificial censoring, which is a popular way to handle dependent censoring, under flexible model assumptions on the two types of events. The proposed method is applied to analyse a data set of bone marrow transplantation.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:scjsta:v:36:y:2009:i:3:p:481-500
    DOI: 10.1111/j.1467-9469.2008.00635.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.
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    3. Debashis Ghosh & D. Y. Lin, 2003. "Semiparametric Analysis of Recurrent Events Data in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 59(4), pages 877-885, December.
    4. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(2), pages 341-353, June.
    5. D. Y. Lin & L. J. Wei & Z. Ying, 2002. "Model-Checking Techniques Based on Cumulative Residuals," Biometrics, The International Biometric Society, vol. 58(1), pages 1-12, March.
    6. Shu-Hui Chang, 2000. "A Two-Sample Comparison for Multiple Ordered Event Data," Biometrics, The International Biometric Society, vol. 56(1), pages 183-189, March.
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    Cited by:

    1. Kyu Ha Lee & Virginie Rondeau & Sebastien Haneuse, 2017. "Accelerated failure time models for semi‐competing risks data in the presence of complex censoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1401-1412, December.
    2. 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.
    3. Jin-Jian Hsieh & A. Adam Ding & Weijing Wang, 2011. "Regression Analysis for Recurrent Events Data under Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(3), pages 719-729, September.
    4. Hsieh, Jin-Jian & Hsu, Chia-Hao, 2018. "Estimation of the survival function with redistribution algorithm under semi-competing risks data," Statistics & Probability Letters, Elsevier, vol. 132(C), pages 1-6.
    5. Heuchenne, Cedric & Laurent, Stephane & Legrand, Catherine & Van Keilegom, Ingrid, 2011. "Likelihood based inference for semi-competing risks," LIDAM Discussion Papers ISBA 2011022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Li, Ruosha & Peng, Limin, 2014. "Varying coefficient subdistribution regression for left-truncated semi-competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 65-78.

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