Compute survival probabilities and optional confidence limits for
Cox survival models. If x=TRUE, y=TRUE were specified to cph,
confidence limits use the correct formula for any combination of
predictors. Otherwise, if surv=TRUE was specified to cph,
confidence limits are based only on standard errors of log(S(t))
at the mean value of \(X\beta\). If the model
contained only stratification factors, or if predictions are being
requested near the mean of each covariable, this approximation will be
accurate. Unless times is given, at most one observation may be
predicted.
survest(fit, …)
# S3 method for cph
survest(fit, newdata, linear.predictors, x, times,
fun, loglog=FALSE, conf.int=0.95, type, vartype,
conf.type=c("log", "log-log", "plain", "none"), se.fit=TRUE,
what=c('survival','parallel'),
individual=FALSE, ...)a model fit from cph
a data frame containing predictor variable combinations for which predictions are desired
a vector of linear predictor values (centered) for which predictions are desired. If the model is stratified, the "strata" attribute must be attached to this vector (see example).
a design matrix at which to compute estimates, with any strata attached
as a "strata" attribute. Only one of newdata,
linear.predictors, or x may be specified. If none is
specified, but times is specified, you will get survival
predictions at all subjects' linear predictor and strata values.
a vector of times at which to get predictions. If omitted, predictions are made at all unique failure times in the original input data.
set to TRUE to make the log-log transformation of survival
estimates and confidence limits.
any function to transform the estimates and confidence limits (loglog
is a special case)
set to FALSE or 0 to suppress confidence limits, or e.g. .95 to
cause 0.95 confidence limits to be computed
see survfit.coxph
see survfit.coxph
specifies the basis for computing confidence limits. "log" is the
default as in the survival package.
set to TRUE to get standard errors of log predicted survival
(no matter what conf.type is).
If FALSE, confidence limits are suppressed.
set to TRUE to have survfit interpret newdata as
specifying a covariable path for a single individual (represented by
multiple records).
Normally use what="survival" to estimate survival probabilities at
times that may not correspond to the subjects' own times.
what="parallel" assumes that the length of times is the number of
subjects (or one), and causes survest to estimate the ith subject's survival
probability at the ith value of times (or at the scalar value of times).
what="parallel" is used by val.surv for example.
unused
If times is omitted, returns a list with the elements
time, n.risk, n.event, surv, call
(calling statement), and optionally std.err, upper,
lower, conf.type, conf.int. The estimates in this
case correspond to one subject. If times is specified, the
returned list has possible components time, surv,
std.err, lower, and upper. These will be matrices
(except for time) if more than one subject is being predicted,
with rows representing subjects and columns representing times.
If times has only one time, these are reduced to vectors with
the number of elements equal to the number of subjects.
The result is passed through naresid if newdata,
linear.predictors, and x are not specified, to restore
placeholders for NAs.
# NOT RUN {
# Simulate data from a population model in which the log hazard
# function is linear in age and there is no age x sex interaction
# Proportional hazards holds for both variables but we
# unnecessarily stratify on sex to see what happens
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n, TRUE))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
Srv <- Surv(dt,e)
f <- cph(Srv ~ age*strat(sex), x=TRUE, y=TRUE) #or surv=T
survest(f, expand.grid(age=c(20,40,60),sex=c("Male","Female")),
times=c(2,4,6), conf.int=.9)
f <- update(f, surv=TRUE)
lp <- c(0, .5, 1)
f$strata # check strata names
attr(lp,'strata') <- rep(1,3) # or rep('sex=Female',3)
survest(f, linear.predictors=lp, times=c(2,4,6))
# Test survest by comparing to survfit.coxph for a more complex model
f <- cph(Srv ~ pol(age,2)*strat(sex), x=TRUE, y=TRUE)
survest(f, data.frame(age=median(age), sex=levels(sex)), times=6)
age2 <- age^2
f2 <- coxph(Srv ~ (age + age2)*strata(sex))
new <- data.frame(age=median(age), age2=median(age)^2, sex='Male')
summary(survfit(f2, new), times=6)
new$sex <- 'Female'
summary(survfit(f2, new), times=6)
options(datadist=NULL)
# }