glance.survfit: Glance at a(n) survfit object

In tidyverse/broom: Convert Statistical Objects into Tidy Tibbles

Glance at a(n) survfit object

Description

Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

Usage

## S3 method for class 'survfit'
glance(x, ...)

Arguments

x	An survfit object returned from survival::survfit().
...	Additional arguments passed to summary.survfit(). Important arguments include rmean.

Value

A tibble::tibble() with exactly one row and columns:

events	Number of events.
n.max	Maximum number of subjects at risk.
n.start	Initial number of subjects at risk.
nobs	Number of observations used.
records	Number of observations
rmean	Restricted mean (see [survival::print.survfit()]).
rmean.std.error	Restricted mean standard error.
conf.low	lower end of confidence interval on median
conf.high	upper end of confidence interval on median
median	median survival

See Also

glance(), survival::survfit()

Other cch tidiers: glance.cch(), tidy.cch()

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

tidyverse/broom documentation built on March 24, 2024, 11:09 a.m.