Look at the finegray command within the survival package; the competing risks vignette has
coverage of it. The command creates an expanded data set with case weights, such that
coxph() on the new data set = the Fine Gray model for the original data. Anything that
works with coxph is valid on the new data. Caveat -- I don't know mice() well at all: the
dat1 data set below has multiple observations per subject, should the mice() command be
cognizant of this?
Terry Therneau
library(survival)
library(mice)
test1 <- data.frame (time=c(4,3,1,1,2,2,3,5,2,4,5,1,
4,3,1,1,2,2,3,5,2,4,5,1),
status=c(1,1,1,0,2,2,0,0,1,1,2,0,
1,1,1,0,2,2,0,0,1,1,2,0),
x=c(0,2,1,1,NA,NA,0,1,1,2,0,1,
0,2,1,1,NA,NA,0,1,1,2,0,1),
sex=c(0,0,0,NA,1,1,1,1,NA,1,0,0,
0,0,0,NA,1,1,1,1,NA,1,0,0)))
# Endpoint 1, simple, then with mice
fit0 <- copxh(Surv(time, status==1) ~ sex + x, test1)
dat0 <- mice(test1, m=10)
mfit0 <- with(dat0, coxph(Surv(time, status==1) ~ sex + x))
summary(pool(mfit0))
# Endpoint 1, Fine-Gray model
fg1 <- finegray(Surv(time, factor(status)) ~ ., data=test1, etype=1)
fit1 <- coxph(Surv(fgstart, fgstop, fgstatus) ~ sex + x, data=fg1,
weight= fgwt)
dat1 <- mice(fg1, m=10)
mfit1 <- with(dat1, coxph(Surv(fgstart, fgstop, fgstatus) ~ sex + x,
weight=fgwt))
On 01/23/2017 05:00 AM, r-help-requ...@r-project.org wrote:
Here is an example:
# example
library(survival)
library(mice)
library(cmprsk)
test1 <- as.data.frame(list(time=c(4,3,1,1,2,2,3,5,2,4,5,1,
4,3,1,1,2,2,3,5,2,4,5,1),
status=c(1,1,1,0,2,2,0,0,1,1,2,0,
1,1,1,0,2,2,0,0,1,1,2,0),
x=c(0,2,1,1,NA,NA,0,1,1,2,0,1,
0,2,1,1,NA,NA,0,1,1,2,0,1),
sex=c(0,0,0,NA,1,1,1,1,NA,1,0,0,
0,0,0,NA,1,1,1,1,NA,1,0,0)))
dat <- mice(test1,m=10)
#Cox regression: cause 1
models.cox1 <- with(dat,coxph(Surv(time, status==1) ~ x +sex
))
summary(pool(models.cox1))
#Cox regression: cause 1 or 2
models.cox <- with(dat,coxph(Surv(time, status==1 | status==2) ~ x +sex
))
models.cox
summary(pool(models.cox))
# crr()
#Fine-Gray model
models.FG<- with(dat,crr(ftime=time, fstatus=status, cov1=test1[,c(
"x","sex")], failcode=1, cencode=0, variance=TRUE))
summary(pool(models.FG))
#Error in pool(models.FG) : Object has no vcov() method.
models.FG
-- Andreu Ferrero Gregori
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