survest.psm {Design} | R Documentation

## 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 `"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.

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu

### See Also

`psm`, `survreg`, `Design`, `survfit`, `predict.Design`, `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

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