10.9 Competing risks regression

Competing-risks regression is an alternative to CPH regression. It can be useful if the outcome of interest may not be able to occur simply because something else (like death) has happened first. For instance, in our example it is obviously not possible for a patient to die from melanoma if they have died from another disease first. By simply looking at cause-specific mortality (deaths from melanoma) and considering other deaths as censored, bias may result in estimates of the influence of predictors.

The approach by Fine and Gray is one option for dealing with this. It is implemented in the package cmprsk. The crr() syntax differs from survival::coxph() but finalfit brings these together.

It uses the finalfit::ff_merge() function, which can join any number of models together.

## Dependent variable is a survival object
TABLE 6.4: Cox Proportional Hazards and competing risks regression combined
Dependent: Survival all HR (DSS CPH univariable) HR (DSS CPH multivariable) HR (competing risks multivariable)
Age (years) Mean (SD) 52.5 (16.7) 1.01 (1.00-1.03, p=0.141) 1.01 (1.00-1.03, p=0.141) 1.01 (0.99-1.02, p=0.520)
Sex Female 126 (61.5)
Male 79 (38.5) 1.54 (0.91-2.60, p=0.106) 1.54 (0.91-2.60, p=0.106) 1.50 (0.87-2.57, p=0.140)
Tumour thickness (mm) Mean (SD) 2.9 (3.0) 1.12 (1.04-1.20, p=0.004) 1.12 (1.04-1.20, p=0.004) 1.09 (1.01-1.18, p=0.019)
Ulcerated tumour No 115 (56.1)
Yes 90 (43.9) 3.20 (1.75-5.88, p<0.001) 3.20 (1.75-5.88, p<0.001) 3.09 (1.71-5.60, p<0.001)