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Stat Med. 2017 Nov 30;36(27):4391-4400. doi: 10.1002/sim.7501. Epub 2017 Sep 15.

Practical recommendations for reporting Fine-Gray model analyses for competing risk data.

Austin PC1,2,3, Fine JP4,5.

Author information

1
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.
2
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
3
Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada.
4
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.
5
Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina, USA.

Abstract

In survival analysis, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Outcomes in medical research are frequently subject to competing risks. In survival analysis, there are 2 key questions that can be addressed using competing risk regression models: first, which covariates affect the rate at which events occur, and second, which covariates affect the probability of an event occurring over time. The cause-specific hazard model estimates the effect of covariates on the rate at which events occur in subjects who are currently event-free. Subdistribution hazard ratios obtained from the Fine-Gray model describe the relative effect of covariates on the subdistribution hazard function. Hence, the covariates in this model can also be interpreted as having an effect on the cumulative incidence function or on the probability of events occurring over time. We conducted a review of the use and interpretation of the Fine-Gray subdistribution hazard model in articles published in the medical literature in 2015. We found that many authors provided an unclear or incorrect interpretation of the regression coefficients associated with this model. An incorrect and inconsistent interpretation of regression coefficients may lead to confusion when comparing results across different studies. Furthermore, an incorrect interpretation of estimated regression coefficients can result in an incorrect understanding about the magnitude of the association between exposure and the incidence of the outcome. The objective of this article is to clarify how these regression coefficients should be reported and to propose suggestions for interpreting these coefficients.

KEYWORDS:

competing risks; cumulative incidence function; subdistribution hazard model; survival analysis

PMID:
28913837
PMCID:
PMC5698744
DOI:
10.1002/sim.7501
[Indexed for MEDLINE]
Free PMC Article

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