survest.psm
Parametric Survival Estimates
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.
- Keywords
- models, regression, survival
Usage
# 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, …)
Arguments
- fit
fit from
psm
- newdata, linear.predictors, x, times, conf.int
see
survest.cph
. One ofnewdata
,linear.predictors
,x
must be given.linear.predictors
includes the intercept. Iftimes
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 fromsurvest.psm
.- what
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 oftimes
is the number of subjects (or one), and causessurvest
to estimate the \(i^{th}\) subject's survival probability at the \(i^{th}\) value oftimes
(or at the scalar value oftimes
).what="parallel"
is used byval.surv
for example.- loglog
set to
TRUE
to transform survival estimates and confidence limits using log-log- fun
a function to transform estimates and optional confidence intervals
- …
unused
Details
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.
Value
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.
See Also
psm
, survreg
, rms
, survfit
, predictrms
, survplot
,
survreg.distributions
Examples
# 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
# }