The broom package exports a tidier for "survfit" objects.
This function adds on top of that and returns more information.
The function also utilizes additional information stored when the
survfit object is created with survfit2().
It's recommended to always use this function with survfit2().
Usage
tidy_survfit(x, times = NULL, type = c("survival", "risk", "cumhaz"))Arguments
- x
a 'survfit' object created with
survfit2()- times
numeric vector of times. Default is
NULL, which returns all observed times.- type
type of statistic to report. Available for Kaplan-Meier estimates only. Default is
"survival". Must be one of the following:type transformation "survival"x"risk"1 - x"cumhaz"-log(x)
Examples
survfit2(Surv(time, status) ~ factor(ph.ecog), data = df_lung) %>%
tidy_survfit()
#> # A tibble: 220 × 16
#> time n.risk n.event n.censor cum.event cum.censor estimate std.error
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 63 0 0 0 0 1 0
#> 2 0.164 63 1 0 1 0 0.984 0.0160
#> 3 0.361 62 1 0 2 0 0.968 0.0228
#> 4 0.493 61 1 0 3 0 0.952 0.0282
#> 5 1.02 60 1 0 4 0 0.937 0.0328
#> 6 1.74 59 1 0 5 0 0.921 0.0370
#> 7 2.14 58 1 0 6 0 0.905 0.0409
#> 8 2.66 57 1 0 7 0 0.889 0.0445
#> 9 4.83 56 1 0 8 0 0.873 0.0480
#> 10 5.45 55 1 0 9 0 0.857 0.0514
#> # ℹ 210 more rows
#> # ℹ 8 more variables: conf.high <dbl>, conf.low <dbl>, strata <fct>,
#> # estimate_type <chr>, estimate_type_label <chr>, monotonicity_type <chr>,
#> # strata_label <chr>, conf.level <dbl>