# survest.psm

From rms v2.0-2
by Frank E Harrell Jr

##### 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 class 'psm':
survest(fit, newdata, linear.predictors, x, times, fun,
loglog=FALSE, conf.int=0.95,
what=c("survival","hazard","parallel"), ...)
```## S3 method for class 'survest.psm':
print(x, \dots)

##### Arguments

- fit
- fit from
`psm`

- newdata, linear.predictors, x, times, conf.int
- 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 po - 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 of`times`

is the number of subjects ( - 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`"survfit"`

so the survival curve can be plotted with survplot.survfit. 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

```
# 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
```

*Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)*

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