```
set.seed(1)
x <- round(rnorm(200))
y <- rnorm(200)
rcorr.cens(x, y, outx=TRUE) # can correlate non-censored variables
library(survival)
age <- rnorm(400, 50, 10)
bp <- rnorm(400,120, 15)
bp[1] <- NA
d.time <- rexp(400)
cens <- runif(400,.5,2)
death <- d.time <= cens
d.time <- pmin(d.time, cens)
rcorr.cens(age, Surv(d.time, death))
r <- rcorrcens(Surv(d.time, death) ~ age + bp)
r
plot(r)
# Show typical 0.95 confidence limits for ROC areas for a sample size
# with 24 events and 62 non-events, for varying population ROC areas
# Repeat for 138 events and 102 non-events
set.seed(8)
par(mfrow=c(2,1))
for(i in 1:2) {
n1 <- c(24,138)[i]
n0 <- c(62,102)[i]
y <- c(rep(0,n0), rep(1,n1))
deltas <- seq(-3, 3, by=.25)
C <- se <- deltas
j <- 0
for(d in deltas) {
j <- j + 1
x <- c(rnorm(n0, 0), rnorm(n1, d))
w <- rcorr.cens(x, y)
C[j] <- w['C Index']
se[j] <- w['S.D.']/2
}
low <- C-1.96*se; hi <- C+1.96*se
print(cbind(C, low, hi))
errbar(deltas, C, C+1.96*se, C-1.96*se,
xlab='True Difference in Mean X',
ylab='ROC Area and Approx. 0.95 CI')
title(paste('n1=',n1,' n0=',n0,sep=''))
abline(h=.5, v=0, col='gray')
true <- 1 - pnorm(0, deltas, sqrt(2))
lines(deltas, true, col='blue')
}
par(mfrow=c(1,1))
```

Run the code above in your browser using DataLab