survest.cph | R Documentation |
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 class '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, ...)
fit |
a model fit from |
newdata |
a data frame containing predictor variable combinations for which predictions are desired |
linear.predictors |
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). |
x |
a design matrix at which to compute estimates, with any strata attached
as a "strata" attribute. Only one of |
times |
a vector of times at which to get predictions. If omitted, predictions are made at all unique failure times in the original input data. |
loglog |
set to |
fun |
any function to transform the estimates and confidence limits ( |
conf.int |
set to |
type |
see |
vartype |
see |
conf.type |
specifies the basis for computing confidence limits. |
se.fit |
set to |
individual |
set to |
what |
Normally use |
... |
unused |
The result is passed through naresid
if newdata
,
linear.predictors
, and x
are not specified, to restore
placeholders for NA
s.
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.
Frank Harrell
Department of Biostatistics
Vanderbilt University
fh@fharrell.com
cph
, survfit.cph
, survfit.coxph
, predictrms
, survplot
# 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
require(survival)
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)
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