Thank you for your reply. It wasn't too hard to code actually, which is probably why it doesn't have a special package dedicated to it. The results are almost identical to Fine & Gray regression model. The problem with the latter is that my colleagues are not convinced that the model assumptions (people who die from competing causes remaining in the risk set) are theoretically sound.
If anybody is interested in the Kalbfleisch & Prentice based cumulative incidence adjusting for competing risks with covariates, I'm happy to supply the code. Eleni Rapsomaniki Research Associate Tel: +44 (0) 1223 740273 Strangeways Research Laboratory Department of Public Health and Primary Care University of Cambridge -----Original Message----- From: Arthur Allignol [mailto:arthur.allig...@fdm.uni-freiburg.de] Sent: 26 March 2009 10:36 To: Eleni Rapsomaniki Cc: r-help@r-project.org Subject: Re: [R] Competing risks Kalbfleisch & Prentice method I don't think there is a package to do that. But you could have a look at ?predict.crr. Best regards, Arthur Allignol Eleni Rapsomaniki wrote: > > > 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 > > > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
