rms: survest.psm – R documentation

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survest.psm

Parametric Survival Estimates

Description

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.

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, ...)

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 points between 0 and the maximum failure/censoring time used to fit the model. `x` can also be a result from `survest.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 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.
`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.

Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
fh@fharrell.com

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

rms

Regression Modeling Strategies

v6.2-0

GPL (>= 2)

Authors

Frank E Harrell Jr <fh@fharrell.com>

Initial release

2021-03-17