Analysis results data for survival quantiles and x-year survival estimates, extracted
from a survival::survfit() model.
ard_survival_survfit(x, ...)
# S3 method for class 'survfit'
ard_survival_survfit(x, times = NULL, probs = NULL, type = NULL, ...)
# S3 method for class 'data.frame'
ard_survival_survfit(
x,
y,
variables = NULL,
times = NULL,
probs = NULL,
type = NULL,
method.args = list(conf.int = 0.95, conf.type = "log"),
...
)(survfit or data.frame)
an object of class survfit created with survival::survfit() or a data frame. See below for details.
These dots are for future extensions and must be empty.
(numeric)
a vector of times for which to return survival probabilities.
(numeric)
a vector of probabilities with values in (0,1) specifying the survival quantiles to return.
(string or NULL)
type of statistic to report. Available for Kaplan-Meier time estimates only, otherwise type
is ignored. Default is NULL.
Must be one of the following:
| type | transformation |
"survival" | x |
"risk" | 1 - x |
"cumhaz" | -log(x) |
(Surv or string)
an object of class Surv created using survival::Surv(). This object will be passed as the left-hand side of
the formula constructed and passed to survival::survfit(). This object can also be passed as a string.
(tidy-select)
stratification variables to be passed as the right-hand side of the formula constructed and passed to
survival::survfit(). Default is NULL for an unstratified model, e.g. Surv() ~ 1.
(named list)
named list of arguments that will be passed to survival::survfit().
an ARD data frame of class 'card'
Only one of either the times or probs parameters can be specified.
Times should be provided using the same scale as the time variable used to fit the provided survival fit model.
When passing a survival::survfit() object to ard_survival_survfit(),
the survfit() call must use an evaluated formula and not a stored formula.
Including a proper formula in the call allows the function to accurately
identify all variables included in the estimation. See below for examples:
library(cardx)
library(survival)
# include formula in `survfit()` call
survfit(Surv(time, status) ~ sex, lung) |> ard_survival_survfit(time = 500)
# you can also pass a data frame to `ard_survival_survfit()` as well.
lung |>
ard_survival_survfit(y = Surv(time, status), variables = "sex", time = 500)You cannot, however, pass a stored formula, e.g. survfit(my_formula, lung),
but you can use stored formulas with rlang::inject(survfit(!!my_formula, lung)).
When the survfit method is called, the class of the stratifying variables
will be returned as a factor.
When the data frame method is called, the original classes are retained in the resulting ARD.
library(survival)
library(ggsurvfit)
survfit(Surv_CNSR(AVAL, CNSR) ~ TRTA, data = cards::ADTTE) |>
ard_survival_survfit(times = c(60, 180))
#> {cards} data frame: 32 x 11
#> group1 group1_level variable variable_level stat_name stat_label stat
#> 1 TRTA Placebo time 60 n.risk Number o… 59
#> 2 TRTA Placebo time 60 estimate Survival… 0.768
#> 3 TRTA Placebo time 60 std.error Standard… 0.047
#> 4 TRTA Placebo time 60 conf.high CI Upper… 0.866
#> 5 TRTA Placebo time 60 conf.low CI Lower… 0.682
#> 6 TRTA Placebo time 180 n.risk Number o… 35
#> 7 TRTA Placebo time 180 estimate Survival… 0.626
#> 8 TRTA Placebo time 180 std.error Standard… 0.056
#> 9 TRTA Placebo time 180 conf.high CI Upper… 0.746
#> 10 TRTA Placebo time 180 conf.low CI Lower… 0.526
#> ℹ 22 more rows
#> ℹ Use `print(n = ...)` to see more rows
#> ℹ 4 more variables: context, fmt_fn, warning, error
survfit(Surv_CNSR(AVAL, CNSR) ~ TRTA, data = cards::ADTTE, conf.int = 0.90) |>
ard_survival_survfit(probs = c(0.25, 0.5, 0.75))
#> {cards} data frame: 29 x 11
#> group1 group1_level variable variable_level stat_name stat_label stat
#> 1 TRTA Placebo prob 0.25 estimate Survival… 70
#> 2 TRTA Placebo prob 0.25 conf.high CI Upper… 110
#> 3 TRTA Placebo prob 0.25 conf.low CI Lower… 42
#> 4 TRTA Placebo prob 0.5 estimate Survival… NA
#> 5 TRTA Placebo prob 0.5 conf.high CI Upper… NA
#> 6 TRTA Placebo prob 0.5 conf.low CI Lower… NA
#> 7 TRTA Placebo prob 0.75 estimate Survival… NA
#> 8 TRTA Placebo prob 0.75 conf.high CI Upper… NA
#> 9 TRTA Placebo prob 0.75 conf.low CI Lower… NA
#> 10 TRTA Xanomeli… prob 0.25 estimate Survival… 14
#> ℹ 19 more rows
#> ℹ Use `print(n = ...)` to see more rows
#> ℹ 4 more variables: context, fmt_fn, warning, error
cards::ADTTE |>
ard_survival_survfit(y = Surv_CNSR(AVAL, CNSR), variables = c("TRTA", "SEX"), times = 90)
#> {cards} data frame: 32 x 13
#> group1 group1_level group2 group2_level variable variable_level stat_name
#> 1 TRTA Placebo SEX F time 90 n.risk
#> 2 TRTA Placebo SEX F time 90 estimate
#> 3 TRTA Placebo SEX F time 90 std.error
#> 4 TRTA Placebo SEX F time 90 conf.high
#> 5 TRTA Placebo SEX F time 90 conf.low
#> 6 TRTA Placebo SEX M time 90 n.risk
#> 7 TRTA Placebo SEX M time 90 estimate
#> 8 TRTA Placebo SEX M time 90 std.error
#> 9 TRTA Placebo SEX M time 90 conf.high
#> 10 TRTA Placebo SEX M time 90 conf.low
#> stat_label stat
#> 1 Number o… 27
#> 2 Survival… 0.619
#> 3 Standard… 0.072
#> 4 CI Upper… 0.777
#> 5 CI Lower… 0.493
#> 6 Number o… 22
#> 7 Survival… 0.748
#> 8 Standard… 0.077
#> 9 CI Upper… 0.916
#> 10 CI Lower… 0.611
#> ℹ 22 more rows
#> ℹ Use `print(n = ...)` to see more rows
#> ℹ 4 more variables: context, fmt_fn, warning, error
# Competing Risks Example ---------------------------
set.seed(1)
ADTTE_MS <- cards::ADTTE %>%
dplyr::mutate(
CNSR = dplyr::case_when(
CNSR == 0 ~ "censor",
runif(dplyr::n()) < 0.5 ~ "death from cancer",
TRUE ~ "death other causes"
) %>% factor()
)
survfit(Surv(AVAL, CNSR) ~ TRTA, data = ADTTE_MS) %>%
ard_survival_survfit(times = c(60, 180))
#> Multi-state model detected. Showing probabilities into state 'death from
#> cancer'.
#> {cards} data frame: 32 x 11
#> group1 group1_level variable variable_level stat_name stat_label stat
#> 1 TRTA Placebo time 60 n.risk Number o… 59
#> 2 TRTA Placebo time 60 estimate Survival… 0.054
#> 3 TRTA Placebo time 60 std.error Standard… 0.026
#> 4 TRTA Placebo time 60 conf.high CI Upper… 0.14
#> 5 TRTA Placebo time 60 conf.low CI Lower… 0.021
#> 6 TRTA Placebo time 180 n.risk Number o… 35
#> 7 TRTA Placebo time 180 estimate Survival… 0.226
#> 8 TRTA Placebo time 180 std.error Standard… 0.054
#> 9 TRTA Placebo time 180 conf.high CI Upper… 0.361
#> 10 TRTA Placebo time 180 conf.low CI Lower… 0.142
#> ℹ 22 more rows
#> ℹ Use `print(n = ...)` to see more rows
#> ℹ 4 more variables: context, fmt_fn, warning, error