val.surv.s
In rms: Regression Modeling Strategies

require(rms)
require(survival)
set.seed(123)              # so can reproduce results
n <- 2500
age <- 50 + 12*rnorm(n)
sex <- factor(sample(c('Male','Female'), n, rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
t <- -log(runif(n))/h
units(t) <- 'Year'
label(t) <- 'Time to Event'
ev <- ifelse(t <= cens, 1, 0)
t <- pmin(t, cens)
S <- Surv(t, ev)
d <- data.frame(t, ev, sex, age)
 
ddist <- datadist(d); options(datadist="ddist")
 
fit <- cph(Surv(t,ev) ~ sex + age, data=d, subset=1:1500,
           surv=TRUE, x=TRUE, y=TRUE, time.inc=5)
 
vd <- d[-(1:1500),]
vs <- val.surv(fit, vd, S=with(vd, Surv(t, ev)), u=5)
par(mfrow=c(2,1))
plot(vs)
 
g <- survest(fit, vd, times=5)
vs <- val.surv(fit, vd, S=with(vd, Surv(t,ev)), u=5, est.surv=g$surv)
plot(vs)

## From Aida Eslama <Aida.Eslami@fmed.ulaval.ca>

d  <- read.csv("val.surv.data.txt", sep="")
n = nrow(d)

## Choix d'un modele avec le BIC

f = survreg(Surv(TIMEDTH, DEATH) ~ CURSMOKE + SEX + BMI + log(AGE),
                      dist = "weibull", data = d, y=TRUE)
f$y[1:10]
AIC(f, k = log(n)); #1586.518

## Verification des hypotheses

f = psm(Surv(TIMEDTH, DEATH) ~ CURSMOKE + SEX + BMI + log(AGE),
                  dist = "weibull", data = d, x = TRUE, y = TRUE)
f$y[1:10]

f$coefficients
std.resid = residuals(f, type = "censored.normalized")[,1];
summary(std.resid)
val.surv(f)

cox.resid = -log(val.surv(f)$est.surv)
summary(cox.resid)
head(cox.resid, 20)