Tidy a(n) survdiff 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 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.

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

Arguments

x

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

...

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.

See also

tidy(), survival::survdiff()

Other survdiff tidiers: glance.survdiff()

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.survexp(), tidy.survfit(), tidy.survreg()

Value

A tibble::tibble() with columns:

exp

Weighted expected number of events in each group.

N

Number of subjects in each group.

obs

weighted observed number of events in each group.

Examples

library(survival)

s <- survdiff(
  Surv(time, status) ~ pat.karno + strata(inst),
  data = lung
)

tidy(s)
#> # A tibble: 8 × 4
#>   pat.karno     N   obs    exp
#>   <chr>     <dbl> <dbl>  <dbl>
#> 1 30            2     1  0.692
#> 2 40            2     1  1.10 
#> 3 50            4     4  1.17 
#> 4 60           30    27 16.3  
#> 5 70           41    31 26.4  
#> 6 80           50    38 41.9  
#> 7 90           60    38 47.2  
#> 8 100          35    21 26.2  
glance(s)
#> # A tibble: 1 × 3
#>   statistic    df p.value
#>       <dbl> <dbl>   <dbl>
#> 1      21.4     7 0.00326