tidy.survfit: Tidy a(n) survfit object

In broom: Convert Statistical Objects into Tidy Tibbles

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 class 'survfit'
tidy(x, ...)

Arguments

x	An survfit object returned from survival::survfit().
...	For glance.survfit(), additional arguments passed to summary(). Otherwise ignored.

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

# load libraries for models and data
library(survival)

# fit model
cfit <- coxph(Surv(time, status) ~ age + sex, lung)
sfit <- survfit(cfit)

# summarize model fit with tidiers + visualization
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)

broom documentation built on Sept. 30, 2024, 9:29 a.m.