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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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