Competing risks Kalbfleisch & Prentice method
Hi Eleni, I would like to take a look at your R function for obtaining the cumulative incidence function (CIF) from individual Cox models for cause-specific hazards (CSH). Does your code predict the CIF (with pointwise confidence intervals and global confidence bands) for different sub-groups? Have you seen the paper by Cheng, Fine, and Wei (Biometrics 1998) that does this? A major advantage of the F&G model is that you can get a direct, numerical measure of the effect of a covariate on the CIF. This cannot be obtained by modeling all the CSHs and then combining them. The idiosyncratic assumption concerning risk set in F&G model is made mainly for mathematical purposes so that a proportional hazards form may be obtained for the CIF. You can test this assumption by plotting schonefeld-type residuals (this is available in cmprsk). Fine (Biostatistics 2006) provides a different approach that relaxes this assumption (it also uses a different estimation approach), but I don't know if there is an R implementation for that. Thanks, Ravi. ---------------------------------------------------------------------------- ------- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging and Health Division of Geriatric Medicine and Gerontology Johns Hopkins University Ph: (410) 502-2619 Fax: (410) 614-9625 Email: rvaradhan at jhmi.edu Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html ---------------------------------------------------------------------------- -------- -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Eleni Rapsomaniki Sent: Thursday, March 26, 2009 10:18 AM To: Arthur Allignol Cc: r-help at r-project.org Subject: Re: [R] Competing risks Kalbfleisch & Prentice method 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.allignol at fdm.uni-freiburg.de] Sent: 26 March 2009 10:36 To: Eleni Rapsomaniki Cc: r-help at 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]]
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______________________________________________ R-help at 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.