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
[[alternative HTML version deleted]]
______________________________________________
[email protected] 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.
