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Competing Risks Model with Short-Term and Long-Term Covariate Effects for Cancer Studies

Author

Listed:
  • Guoqing Diao

    (George Mason University)

  • Anand N. Vidyashankar

    (George Mason University)

  • Sarah Zohar

    (Sorbonne Université, Université de Paris)

  • Sandrine Katsahian

    (Sorbonne Université, Université de Paris
    CIC-EC 1418, Inserm, Hôpital Européen Georges-Pompidou)

Abstract

Patients are frequently exposed to failure from several mutually exclusive causes, leading to a competing risk setting. Standard methods concerning the effects of covariates on the cause-specific hazards assume constant hazard ratios across time. This assumption, however, is violated in several applications. To address this issue and test the effect of covariates on multiple risks, we develop a new regression model allowing for nonconstant hazard ratios over time. The proposed model allows explicit specification of the short-term and long-term covariate effects, which can be of clinical interest. We develop a statistically efficient nonparametric likelihood methodology for estimation and inference concerning the parameters of interest and compare it to the existing methods. We investigate the performances of the proposed methods using simulations and apply them to a European study on a registry cohort of patients with acute leukemia undergoing bone marrow transplantation. Our proposed model detects the differences in short-term and long-term risks of primary relapse between patients with and without acute lymphoblastic leukemia.

Suggested Citation

  • Guoqing Diao & Anand N. Vidyashankar & Sarah Zohar & Sandrine Katsahian, 2021. "Competing Risks Model with Short-Term and Long-Term Covariate Effects for Cancer Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 142-159, April.
    • Handle: RePEc:spr:stabio:v:13:y:2021:i:1:d:10.1007_s12561-020-09288-x
    DOI: 10.1007/s12561-020-09288-x
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    References listed on IDEAS

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    1. Lu Tian & David Zucker & L.J. Wei, 2005. "On the Cox Model With Time-Varying Regression Coefficients," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 172-183, March.
    2. Zongwu Cai & Yanqing Sun, 2003. "Local Linear Estimation for Time‐Dependent Coefficients in Cox's Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 93-111, March.
    3. Murphy, S. A. & Sen, P. K., 1991. "Time-dependent coefficients in a Cox-type regression model," Stochastic Processes and their Applications, Elsevier, vol. 39(1), pages 153-180, October.
    4. Song Yang & Ross Prentice, 2005. "Semiparametric analysis of short-term and long-term hazard ratios with two-sample survival data," Biometrika, Biometrika Trust, vol. 92(1), pages 1-17, March.
    5. Limin Peng & Jason P. Fine, 2007. "Regression Modeling of Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 63(1), pages 96-108, March.
    6. Torben Martinussen & Thomas H. Scheike & Ib M. Skovgaard, 2002. "Efficient Estimation of Fixed and Time‐varying Covariate Effects in Multiplicative Intensity Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 57-74, March.
    7. Limin Peng & Yijian Huang, 2007. "Survival analysis with temporal covariate effects," Biometrika, Biometrika Trust, vol. 94(3), pages 719-733.
    8. Guoqing Diao & Donglin Zeng & Song Yang, 2013. "Efficient Semiparametric Estimation of Short-Term and Long-Term Hazard Ratios with Right-Censored Data," Biometrics, The International Biometric Society, vol. 69(4), pages 840-849, December.
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