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

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.

Education and insurance status: Impact on treatment and survival of sinonasal cancer patients

“…This is an important methodological distinction, which would confound the results when using the Fine and Gray method to calculate SHRs. We are instead left with an analysis of covariates that can help inform associated risk, which is a key finding in the present work and novel to the literature …”

Propensity‐score matching with competing risks in survival analysis

“…The direction of the subdistribution hazard ratio provides information of the direction of treatment on the cumulative incidence of the outcome. However, similarly to survival data without competing risks, the magnitude of this hazard ratio does not provide direct information on the magnitude of the relative effect of the treatment on the cumulative incidence function …”

“…The estimated subdistribution hazard ratio from the clustered Fine‐Gray model was 0.76 (95% confidence interval: (0.67, 0.86)). Similarly to the hazard ratio from the usual Cox proportional hazard model without competing risks, one cannot provide a simple quantification of the relative change in the absolute risk of cardiovascular death due to statin prescribing . However, since the subdistribution hazard ratio is less than one, one can infer that the incidence of cardiovascular death is lower in treated subjects than in matched control subjects.…”

The mRECIST Classification Provides Insight into Tumor Biology for Patients With Hepatocellular Carcinoma Awaiting Liver Transplantation

“…All causes of death or dropout were treated as events and incorporated in the survival models. Competing risk regression was performed using the methods described by Fine and Gray to determine the cumulative incidence of recurrence . Univariate and multivariate odds ratios (ORs) for predictors of post‐LT HCC recurrence were determined by logistic regression.…”