Hein Putter

Dept. of Biomedical Data Sciences, Leiden University Medical Center, the Netherlands

Location: ZOOM

https://cesnet.zoom.us/j/92685451604?pwd=TUJzZTV0V1JXaXNLTlNVaWMvTS9Idz09

Meeting ID: 926 8545 1604

Passcode: 358199

Date: Thursday 21 April 2022

Time: 13:30 CET

Abstract:

The Fine-Gray subdistribution hazard model has become the default method to estimate the incidence of outcomes over time in the presence of competing risks. This model is attractive because it directly relates covariates to the cumulative incidence function (CIF) of the event of interest. An alternative is to combine the different cause-specific hazard functions to obtain the different CIFs. A limitation of the subdistribution hazard approach is that the sum of the cause-specific CIFs can exceed 1 (100%) for some covariate patterns. We provide empirical evidence that this problem is more common than might be imagined. We also provide a proof that the sum of predictions exceeds 1 is a fundamental problem with the Fine-Gray subdistribution hazard model. We conclude that care should be taken when using the Fine-Gray subdistribution hazard model in situations with wide risk distributions or a high cumulative incidence, and if one is interested in the risk of failure from each of the different event types.

Related: https://onlinelibrary.wiley.com/doi/full/10.1002/sim.9023

https://github.com/survival-lumc/FG-TotalFailureProbability

References:

1. Austin, P. C., Steyerberg, E. W. & Putter, H. Fine‐Gray subdistribution hazard models to simultaneously estimate the absolute risk of different event types: Cumulative total failure probability may exceed 1. Stat Med 40, 4200–4212 (2021).

2. Fine, J. P. & Gray, R. J. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association 94, 496–509 (1999).

3. Putter, H., Fiocco, M. & Geskus, R. Tutorial in biostatistics: competing risks and multi‐state models. Statistics in Medicine 26, 2389–2430 (2007).