survest.cph {rms} | R Documentation

## Cox Survival Estimates

### Description

Compute survival probabilities and optional confidence limits for Cox survival models. If `x=TRUE, y=TRUE` were specified to `cph`, confidence limits use the correct formula for any combination of predictors. Otherwise, if `surv=TRUE` was specified to `cph`, confidence limits are based only on standard errors of `log(S(t))` at the mean value of X beta. If the model contained only stratification factors, or if predictions are being requested near the mean of each covariable, this approximation will be accurate. Unless `times` is given, at most one observation may be predicted.

### Usage

    survest(fit, ...)
    ## S3 method for class 'cph'
    survest(fit, newdata, linear.predictors, x, times,
            fun, loglog=FALSE, conf.int=0.95, type, vartype,
            conf.type=c("log", "log-log", "plain", "none"), se.fit=TRUE,
            what=c('survival','parallel'),
            individual=FALSE, ...)

### Arguments

`fit` | a model fit from `cph`
`newdata` | a data frame containing predictor variable combinations for which predictions are desired
`linear.predictors` | a vector of linear predictor values (centered) for which predictions are desired. If the model is stratified, the "strata" attribute must be attached to this vector (see example).
`x` | a design matrix at which to compute estimates, with any strata attached as a "strata" attribute. Only one of `newdata`, `linear.predictors`, or `x` may be specified. If none is specified, but `times` is specified, you will get survival predictions at all subjects' linear predictor and strata values.
`times` | a vector of times at which to get predictions. If omitted, predictions are made at all unique failure times in the original input data.
`loglog` | set to `TRUE` to make the `log-log` transformation of survival estimates and confidence limits.
`fun` | any function to transform the estimates and confidence limits (`loglog` is a special case)
`conf.int` | set to `FALSE` or `0` to suppress confidence limits, or e.g. `.95` to cause 0.95 confidence limits to be computed
`type` | see `survfit.coxph`
`vartype` | see `survfit.coxph`
`conf.type` | specifies the basis for computing confidence limits. `"log"` is the default as in the `survival` package.
`se.fit` | set to `TRUE` to get standard errors of log predicted survival (no matter what `conf.type` is). If `FALSE`, confidence limits are suppressed.
`individual` | set to `TRUE` to have `survfit` interpret `newdata` as specifying a covariable path for a single individual (represented by multiple records).
`what` | Normally use `what="survival"` to estimate survival probabilities at times that may not correspond to the subjects' own times. `what="parallel"` assumes that the length of `times` is the number of subjects (or one), and causes `survest` to estimate the ith subject's survival probability at the ith value of `times` (or at the scalar value of `times`). `what="parallel"` is used by `val.surv` for example.
`...` | unused

### Details

The result is passed through `naresid` if `newdata`, `linear.predictors`, and `x` are not specified, to restore placeholders for `NA`s.

### Value

If `times` is omitted, returns a list with the elements `time`, `n.risk`, `n.event`, `surv`, `call` (calling statement), and optionally `std.err`, `upper`, `lower`, `conf.type`, `conf.int`. The estimates in this case correspond to one subject. If `times` is specified, the returned list has possible components `time`, `surv`, `std.err`, `lower`, and `upper`. These will be matrices (except for `time`) if more than one subject is being predicted, with rows representing subjects and columns representing `times`. If `times` has only one time, these are reduced to vectors with the number of elements equal to the number of subjects.

### Author(s)

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

### See Also

`cph`, `survfit.cph`, `survfit.coxph`, `predictrms`, `survplot`

### Examples

    # Simulate data from a population model in which the log hazard
    # function is linear in age and there is no age x sex interaction
    # Proportional hazards holds for both variables but we
    # unnecessarily stratify on sex to see what happens
    n <- 1000
    set.seed(731)
    age <- 50 + 12*rnorm(n)
    label(age) <- "Age"
    sex <- factor(sample(c('Male','Female'), n, TRUE))
    cens <- 15*runif(n)
    h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
    dt <- -log(runif(n))/h
    label(dt) <- 'Follow-up Time'
    e <- ifelse(dt <= cens,1,0)
    dt <- pmin(dt, cens)
    units(dt) <- "Year"
    dd <- datadist(age, sex)
    options(datadist='dd')
    Srv <- Surv(dt,e)

    f <- cph(Srv ~ age*strat(sex), x=TRUE, y=TRUE) #or surv=T
    survest(f, expand.grid(age=c(20,40,60),sex=c("Male","Female")),
    	    times=c(2,4,6), conf.int=.9)
    f <- update(f, surv=TRUE)
    lp <- c(0, .5, 1)
    f$strata   # check strata names
    attr(lp,'strata') <- rep(1,3)  # or rep('sex=Female',3)
    survest(f, linear.predictors=lp, times=c(2,4,6))

    # Test survest by comparing to survfit.coxph for a more complex model
    f <- cph(Srv ~ pol(age,2)*strat(sex), x=TRUE, y=TRUE)
    survest(f, data.frame(age=median(age), sex=levels(sex)), times=6)

    age2 <- age^2
    f2 <- coxph(Srv ~ (age + age2)*strata(sex))
    new <- data.frame(age=median(age), age2=median(age)^2, sex='Male')
    summary(survfit(f2, new), times=6)
    new$sex <- 'Female'
    summary(survfit(f2, new), times=6)

    options(datadist=NULL)

[Package rms version 5.1-1 Index]