Practical recommendations for reportingFine‐Gray model analyses for competing risk data

Peter C. Austin; Jason P. Fine

doi:10.1002/sim.7501

journal article Open Access Sep 15, 2017

Practical recommendations for reportingFine‐Gray model analyses for competing risk data

Peter C. Austin

Jason P. Fine

Statistics in Medicine Vol. 36 No. 27 pp. 4391-4400 · Wiley

View at Publisher Save 10.1002/sim.7501

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.

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Details

Published: Sep 15, 2017
Vol/Issue: 36(27)
Pages: 4391-4400
License: View

Authors

P

Peter C. Austin

Institute for Clinical Evaluative Sciences Toronto Ontario Canada; Institute of Health Policy, Management and Evaluation University of Toronto Toronto Ontario Canada; Schulich Heart Research Program Sunnybrook Research Institute Toronto Ontario Canada

J

Jason P. Fine

Department of Biostatistics University of North Carolina Chapel Hill North Carolina USA; Department of Statistics and Operations Research University of North Carolina Chapel Hill North Carolina USA

Funding

Canadian Institutes of Health Research Award: CRT43823

Heart and Stroke Foundation of Canada

Ontario Ministry of Health and Long-Term Care

Cite This Article

Peter C. Austin, Jason P. Fine (2017). Practical recommendations for reportingFine‐Gray model analyses for competing risk data. Statistics in Medicine, 36(27), 4391-4400. https://doi.org/10.1002/sim.7501

Practical recommendations for reportingFine‐Gray model analyses for competing risk data

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