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 [[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.