R help - Mar 2013 - Comparing Cox model with Competing Risk model

I have a competing risk data where a patient may die from either AIDS or
Cancer. I want to compare the cox model for each of the event of interest
with a competing risk model. In the competing risk model the cumulative
incidence function is used directly. I used the jackknife (pseudovalue) of
the cumulative incidence function for each cause (AIDS or Cancer) in a
generalized estimating equation. I used the following code:
>library(pseudo)
>cutoffs<-c(0.5,5.5,10.5,15.5)
>pseudo<-pseudoci(time,event,tmax=cutoffs)
>library(geepack)
>fit<-geese(pseudo~as.factor(Age)+as.factor(Sex),data=b,id=id, jack =
TRUE,
scale.fix=TRUE,
mean.link = "logit", corstr="independence").



I want to know whether I am doing the right thing?

	[[alternative HTML version deleted]]

-- begin included message --
I have a competing risk data where a patient may die from either AIDS or
Cancer. I want to compare the cox model for each of the event of interest
with a competing risk model. In the competing risk model the cumulative
incidence function is used directly.

-end inclusion ---
  If you do want to persue the Fine-Gray model I would suggest using software
that already
exists.  Find the "Task Views" tab on CRAN, and follow it to
"survival" and then look at
the competing risks section.  There is a lot to offer.  I would trust it more
than rolling
your own function.

  As an aside, modeling the subdistribution function is ONE way of dealing with
competing
risks, but not everyone thinks that it is the best way to proceed.  The model
corresponds
to a biology that I find unlikely, though it makes for nice math.  Since the
alternative
is discussed in a vignette that I haven't-yet-quite-written we won't
persue that any
further, however. :-)

Terry Therneau