Dear all,

I would like to obtain the Brier score prediction error at different times t
for an extended Cox model. Previously I have used the 'pec' function
(pec{pec}) to obtain prediction error curves for standard Cox PH models but
now I have data in the counting process format (I have a covariate with a
time-varying effect) and it seems that the pec function does not support the
counting process format, or am I doing something wrong?

Here's a (tiny) example of what I'm trying to do:

# Original survival data set:
> dat
  time status x1
1  169      1  2
2  149      1 11
3  207      1 22
4  192      1 27
5  200      1 10

# Split original data at cutpoint 190. New data will be in "counting
process" format:
> dat.x = survSplit(dat, cut=190, end="time", event="status", start="start")

# New data set:
> dat.x
   time status x1 start
1   169      1  2     0
2   149      1 11     0
3   190      0 22     0
4   190      0 27     0
5   190      0 10     0
8   207      1 22   190
9   192      1 27   190
10  200      1 10   190

# Load pec and fit Cox model:
> library(pec)
> models = list("Cox"= coxph(Surv(start,time,status) ~ x1, data=dat.x))

# Compute the apparent prediction error:
predError = pec(object = models, formula=Surv(start,time,status) ~ x1,
data=dat.x, exact=TRUE, cens.model="marginal", replan="none", B=0,
verbose=TRUE)
Error in pec.list(object = models, formula = Surv(start, time, status) ~  :
  Survival response must at least consist of two columns: time and status.
>

Am I doing something wrong here or is it not possible to apply the pec
function on counting process data? If I can't use pec, perhaps someone knows
of some other function I could use instead to get the Brier score at
different times t when using the counting process approach. Any guidance on
these questions is much appreciated.

Thanks,
Ulf

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