Computes predicted survival probabilities or hazards and optionally confidence
limits (for survival only) for parametric survival models fitted with
psm.
If getting predictions for more than one observation, times must
be specified. For a model without predictors, no input data are
specified.
# S3 method for psm
survest(fit, newdata, linear.predictors, x, times, fun,
loglog=FALSE, conf.int=0.95,
what=c("survival","hazard","parallel"), …)# S3 method for survest.psm
print(x, …)
fit from psm
see survest.cph. One of newdata, linear.predictors, x must be given.
linear.predictors includes the intercept.
If times is omitted, predictions are made at 200 equally spaced points
between 0 and the maximum failure/censoring time used to fit the model.
x can also be a result from survest.psm.
The default is to compute survival probabilities. Set what="hazard" or
some abbreviation of "hazard" to compute hazard rates.
what="parallel" assumes that the length of times is the number of
subjects (or one), and causes survest to estimate the
\(i^{th}\) subject's survival probability at the \(i^{th}\) value of
times (or at the scalar value of times).
what="parallel" is used by val.surv for example.
set to TRUE to transform survival estimates and confidence limits using
log-log
a function to transform estimates and optional confidence intervals
unused
see survest.cph. If the model has no predictors, predictions are
made with respect to varying time only, and the returned object
is of class "npsurv" so the survival curve can be plotted
with survplot.npsurv. If times is omitted, the
entire survival curve or hazard from t=0,…,fit$maxtime is estimated, with
increments computed to yield 200 points where fit$maxtime is the
maximum survival time in the data used in model fitting. Otherwise,
the times vector controls the time points used.
Confidence intervals are based on asymptotic normality of the linear predictors. The intervals account for the fact that a scale parameter may have been estimated jointly with beta.
psm, survreg, rms, survfit, predictrms, survplot,
survreg.distributions
# NOT RUN {
# Simulate data from a proportional hazards population model
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
S <- Surv(dt,e)
f <- psm(S ~ lsp(age,c(40,70)))
survest(f, data.frame(age=seq(20,80,by=5)), times=2)
#Get predicted survival curve for 40 year old
survest(f, data.frame(age=40))
#Get hazard function for 40 year old
survest(f, data.frame(age=40), what="hazard")$surv #still called surv
# }