R help - Mar 2009 - Competing risks Kalbfleisch & Prentice method

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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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 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.
>

Ravi's last note finished with
>  I am wondering why Terry Therneau's "survival" package
doesn't
>  have this option.  
  The short answer is that there are only so many hours in a day.  

  I've recently moved the code base from an internal Mayo repository to
R-forge,
one long term goal with this is to broaden the developer base to n>2 (me and 
Thomas Lumley).  
  
  A longer statistical answer:
  
  I'm not sure if the "this" of Ravi's question is a. smoothed
hazards, b. the
K&P cumulative incidence or c. the Fine & Gray model.
  
  b. I like the CI model and am using it more.  We also have local code. The 
latest version of survival (on rforge, likely in the next default R release) has
added simple CI curves to the survfit function.  Adding code for survfit on Cox 
models is on the todo list.  But -- this release also fixes up survfit.coxph to 
handle weighted Cox models and that was on my list for approx 10 years, i.e., 
don't hold your breath.  I don't release something until it also has a
set of
worked out test cases to add to the 'tests' directory.
  
  a. smoothed hazards.  For the case at hand I don't see any particular 
advantage of this.  On the other hand, I often would like to display hazard 
functions instead of CI functions for Cox models; with time dependent covariates
I don't think a survival curve makes sense.  But I haven't had the time
to think
through exactly which methods should be added.
  
  c. Fine & Gray model, i.e., where covariates have a direct influence on
the
competing risk.  I find the model completely untenable from a biologic point of 
view, so have no interest in adding it.  (Due to finite time, everything in the 
survival package is code that I needed for an analysis; medical research is what
pays my salary.)  Assume that I have competing processes/risks, say progression 
of a tumor and heart disease;  I expect that the tumor process pays no attention
whatsoever to what is going on in the heart.  But this is necessary if 
"type=squamous" is modeled as an absolute beta=__ increase in the CI
for cancer.
 The squamous cells need to "step up the pace" of invasion if heart
failure
threatens, like jockeys in a horse race. 
  
   Terry T.