broom (version 0.7.5)

tidy.survfit: Tidy a(n) survfit object

Description

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# S3 method for survfit
tidy(x, ...)

Arguments

x

An survfit object returned from survival::survfit().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.level = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

n.censor

Number of censored events.

n.event

Number of events at time t.

n.risk

Number of individuals at risk at time zero.

std.error

The standard error of the regression term.

time

Point in time.

estimate

estimate of survival or cumulative incidence rate when multistate

state

state if multistate survfit object input

strata

strata if stratified survfit object input

See Also

tidy(), survival::survfit()

Other survival tidiers: augment.coxph(), augment.survreg(), glance.aareg(), glance.cch(), glance.coxph(), glance.pyears(), glance.survdiff(), glance.survexp(), glance.survfit(), glance.survreg(), tidy.aareg(), tidy.cch(), tidy.coxph(), tidy.pyears(), tidy.survdiff(), tidy.survexp(), tidy.survreg()

Examples

# NOT RUN {
library(survival)
cfit <- coxph(Surv(time, status) ~ age + sex, lung)
sfit <- survfit(cfit)

tidy(sfit)
glance(sfit)

library(ggplot2)
ggplot(tidy(sfit), aes(time, estimate)) +
  geom_line() +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25)

# multi-state
fitCI <- survfit(Surv(stop, status * as.numeric(event), type = "mstate") ~ 1,
  data = mgus1, subset = (start == 0)
)
td_multi <- tidy(fitCI)
td_multi

ggplot(td_multi, aes(time, estimate, group = state)) +
  geom_line(aes(color = state)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25)
# }