Tidy a(n) survfit object

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 cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# 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.lvel = 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 one row for each time point and columns:

time

timepoint

n.risk

number of subjects at risk at time t0

n.event

number of events at time t

n.censor

number of censored events

estimate

estimate of survival or cumulative incidence rate when multistate

std.error

standard error of estimate

conf.high

upper end of confidence interval

conf.low

lower end of confidence interval

state

state if multistate survfit object inputted

strata

strata if stratified survfit object inputted

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

library(survival)
cfit <- coxph(Surv(time, status) ~ age + sex, lung)
sfit <- survfit(cfit)

tidy(sfit)
#> # A tibble: 186 x 8
#>     time n.risk n.event n.censor estimate std.error conf.high conf.low
#>    <dbl>  <dbl>   <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl>
#>  1     5    228       1        0    0.996   0.00419     1        0.988
#>  2    11    227       3        0    0.983   0.00845     1.00     0.967
#>  3    12    224       1        0    0.979   0.00947     0.997    0.961
#>  4    13    223       2        0    0.971   0.0113      0.992    0.949
#>  5    15    221       1        0    0.966   0.0121      0.990    0.944
#>  6    26    220       1        0    0.962   0.0129      0.987    0.938
#>  7    30    219       1        0    0.958   0.0136      0.984    0.933
#>  8    31    218       1        0    0.954   0.0143      0.981    0.927
#>  9    53    217       2        0    0.945   0.0157      0.975    0.917
#> 10    54    215       1        0    0.941   0.0163      0.972    0.911
#> # … with 176 more rows
glance(sfit)
#> # A tibble: 1 x 9
#>   records n.max n.start events rmean rmean.std.error median conf.low conf.high
#>     <dbl> <dbl>   <dbl>  <dbl> <dbl>           <dbl>  <dbl>    <dbl>     <dbl>
#> 1     228   228     228    165  381.            20.3    320      285       363

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
#> # A tibble: 474 x 9
#>     time n.risk n.event n.censor estimate std.error conf.high conf.low state
#>    <dbl>  <int>   <int>    <int>    <dbl>     <dbl>     <dbl>    <dbl> <fct>
#>  1     6      0       0        0        0         0        NA       NA 2    
#>  2     7      0       0        0        0         0        NA       NA 2    
#>  3    31      0       0        0        0         0        NA       NA 2    
#>  4    32      0       0        0        0         0        NA       NA 2    
#>  5    39      0       0        0        0         0        NA       NA 2    
#>  6    60      0       0        0        0         0        NA       NA 2    
#>  7    61      0       0        0        0         0        NA       NA 2    
#>  8   152      0       0        0        0         0        NA       NA 2    
#>  9   153      0       0        0        0         0        NA       NA 2    
#> 10   174      0       0        0        0         0        NA       NA 2    
#> # … with 464 more rows

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