Statistical issues in survival analysis (Part XVVIII) February 26, 2024 TABLE 1. Summary of the available methods for survival regression with competing risks (CR). Model
Type
Proportional hazards (PH)
High Missing dimensions (�) data
Approaches based on a cause-specific hazard specification Cox proportional CS hazard
Semiparamet rica
✓
✗
✗
Lunn–McNeil
Semiparamet ric
✓
✗
✗
Penalized Cox PH
Semiparamet ric
✓
✓
✗
Cox model-based boosting
Semiparamet ric
✓
✓
✗
Cox likelihood-based boosting
Semiparamet ric
✓
✓
✗
Fine–Gray
Semiparamet ric
✓
✗
✗
Penalized proportional subdistribution hazard
Semiparamet ric
✓
✓
✗
Approaches based on the CIF
In an article that appeared in Biometrical Journal, Monterruio-Gomez et al presented a review of competing risks (CR) methods in survival analysis and machine learning methods. Competing risks models have traditionally beeen used in survival anlaysis when there is more than one mutually exclusive event of interest. While this has been an active area of interest, the implementation of the methods in software has been limited. They aimed to summarize current landscape of CR approaches developed by statistics and machine learning methods. They first briefly discussed methodology from cumulative incidence functions to subdistribution hazard functions. They especiallly mentioned the Fine and Grey method. Regression models based on latent failure times also exist. In Table 1, they gave a summary of available methods for survival regression with CR. They also discusssed approaches based on a cause-specific (CS)