Competing-risks regression
Competing-risks survival regression provides a useful alternative to Cox
regression in the presence of one or more competing risks. For example, say
that you are studying the time from initial treatment for cancer to recurrence of
cancer in relation to the type of treatment administered and demographic
factors. Death is a competing event: the person under treatment may die,
impeding the occurence of the event of interest, recurrence of cancer.
Unlike censoring, which merely obstructs you from viewing the event, a
competing event prevents the event of interest from occurring altogether,
and your analysis should adjust accordingly.
Stata’s stcrreg command implements competing-risks regression
based on Fine and Gray’s proportional subhazards model. In Cox
regression, you focus on the survivor function, which indicates the
probability of surviving beyond a given time. In competing-risks regression,
you instead focus on the cumulative incidence function, which indicates the
probability of the event of interest happening before a given time.
Competing-risks regression is semiparametric in that the baseline subhazard
of the event of interest is left unspecified, and the effects of covariates
are assumed to be proportional. Time-varying covariates and coefficients
are allowed.
Here we fit a model for cervical cancer patients, where the event of
interest is a relapse of cancer located in the pelvis. A competing event is
the occurrence of cancer in another part of the body.
Stata’s stcurve command allows us to examine the cumulative incidence
function:
. stcurve, cif at1(ifp=5 pelnode=0) at2(ifp=20 pelnode=0)
View a complete list of survival analysis capabilities.
Explore more about competing risks regression
in Stata.
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