System: R 2.3.1 on a Windows XP computer. 

I am validating several cancer prognostic models that have been
published with a large independent dataset.  Some of the models report a
probability of survival at a specified timepoint, usually at 5 and 10
years.  Others report only the linear predictor of the Cox model.

I have used Harrell's c index for censored data (rcorr.cens) as a
measure of discrimination and have constructed smoothed calibration
plots.  I would like to include some measures of overall model
performance, such as the censored Brier score and Royston/Sauerbrei's D
statistic (Stat Med 2004). With this in mind, I have 3 questions:

1) Can the "sbrier" function of the "ipred" library be used to calculate
the censored Brier score for a specific time point given known
predictions for that timepoint?  

library(ipred)

data <- read.csv(file='c:\\....

time    <- data$time  	# The time in years from diagnosis of cancer to
death
status <- data$status 	# The status at last follow-up: 1=dead, 0=alive
pred    <- data$pred	# The predicted probability of surviving 5 years
after cancer from external Cox model A
linp     <- data$linp	# The linear predictor of external Cox model B
predicting survival after cancer 

s <- Surv(time, status)
test <- sbrier(s, pred, btime=5)

# I get this error message that I can't seem to solve
Error in Surv(time, 1 - cens) : Time variable is not numeric
In addition: Warning message:
is.na() applied to non-(list or vector) in: is.na(time)

2) Can the linear predictor (linp) be used in sbrier in the same way as
a probability might?

3) Has anyone implemented Royston/Sauerbrei's D?

Brant Inman
Mayo Clinic


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