rms: survest.cph – R documentation

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survest.cph

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 NAs.

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
fh@fharrell.com

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)

rms

Regression Modeling Strategies

v6.2-0

GPL (>= 2)

Authors

Frank E Harrell Jr <fh@fharrell.com>

Initial release

2021-03-17