Dear all,

Happy New year!

I have used the 'crr' function to fit the 'proportional subdistribution
hazards' regression model described in Fine and Gray (1999).

dat1 is a three column dataset where:
- ccr is the time to event variable
- Crcens is an indicator variable equal to 0 if the event was achieved, 1
if the event wasn't acheived due to death or 2 if the event wasn't achieved
due to disease progression
- pre is an indicator variable (and the covariate of interest)

I want to investigate if pre has a significant impact on time to event for
patients who died and for those who suffered disease progression (as well
as it's impact on the overall time to event).

The code I have used is as follows:

fitd <- crr(dat1$ccr,dat1$Crcens,dat1$pre,failcode=1,cencode=0)
fitp <- crr(dat1$ccr,dat1$Crcens,dat1$pre,failcode=2,cencode=0)

In these cases I get p-values of 0 and 0.66 respectively.

What I would now like to do, is to plot two cumulative incidence curves -
one for the 'pre' variable status for patients who didn't acheive the event
due to death and one for those who didn't achieve it due to progression.

How can I do this?  I can only see things involving plot.predict.crr which
doesn't seem to be what I need?


Many thanks,
Laura

Usung Windows 7 and R 2.14.1

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