Dear R users
I would like to calculate the Cumulative incidence for an event
adjusting for competing risks and adjusting for covariates. One way to
do this in R is to use the cmprsk package, function crr. This uses the
Fine & Gray regression model. However, a simpler and more classical
approach would be to implement the Kalbfleisch & Prentice method (1980,
p 169), where one fits cause specific cox models for the event of
interest and each type of competing risk, and then calculates incidence
based on the overall survival. I believe that this is what the cuminc
function in the aforementioned package does, but it does not allow to
adjust for a vector of covariates.
My question is, is there an R package that implements the Kalbfleisch &
Prentice method for competing risks with covariates?
for example, if k1 is the cause of interest among k competing causes:
P_k1(t; x)=P(T<=t, cause=k1|x)=Sum(u=0, ..., u=t) {hazard_k(u;x)*S(u;x)}
where S(u;x) = exp{-sum_of_k(sum(hazard_k(u))}
I have searched extensively for an implementation of this in many
packages, but it appears that more complex approaches are more commonly
implemented, such as timereg package.
Eleni Rapsomaniki
Research Associate
Strangeways Research Laboratory
Department of Public Health and Primary Care
University of Cambridge
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