Comparison of the Marginal Hazard Model and the Sub-distribution Hazard Model for Competing Risks under an Assumed Copula

Takeshi Emura, Jia-Han Shih, Il Do Ha, Ralf Wilke

Publikation: Bidrag til tidsskriftTidsskriftartikel

Abstrakt

For the analysis of competing risks data, three different types of hazard functions have been considered in the literature, namely the cause-specific hazard, the sub-distribution hazard, and the marginal hazard function. Accordingly, medical researchers can fit three different types of the Cox model to estimate the effect of covariates on each of the hazard function. While the relationship between the cause-specific hazard and the sub-distribution hazard has been extensively studied, the relationship to the marginal hazard function has not yet been analyzed due to the difficulties related to non-identifiability. In this paper, we adopt an assumed copula model to deal with the model identifiability issue, making it possible to establish a relationship between the sub-distribution hazard and the marginal hazard function. We then compare the two methods of fitting the Cox model to competing risks data. We also extend our comparative analysis to clustered competing risks data that are frequently used in medical studies. To facilitate the numerical comparison, we implement the computing algorithm for marginal Cox regression with clustered competing risks data in the R joint.Cox package and check its performance via simulations. For illustration, we analyze two survival datasets from lung cancer and bladder cancer patients
OriginalsprogEngelsk
TidsskriftStatistical Methods in Medical Research
Antal sider21
ISSN0962-2802
DOI
StatusUdgivet - 22 dec. 2019

Bibliografisk note

Epub ahead of print. Published online: December 22, 2019

Emneord

  • Clustered survival data
  • Competing risk
  • Cox model
  • Frailty model
  • Survival analysis

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