Learn R Programming

junco (version 0.1.2)

event_free: Workaround statistics function to time point survival estimate with CI

Description

This is a workaround for tern::s_surv_timepoint(), which adds a statistic containing the time point specific survival estimate together with the confidence interval.

Usage

a_event_free(
  df,
  .var,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_event_free( df, .var, time_point, time_unit, is_event, percent = FALSE, control = control_surv_timepoint() )

Value

  • s_event_free returns a list as returned by the tern::s_surv_timepoint() with an additional three-dimensional statistic event_free_ci which combines the event_free_rate and rate_ci statistics.

  • a_event_free is analogous to tern::a_surv_timepoint but with the additional three-dimensional statistic described above available via .stats.

Arguments

df

(data.frame)
data set containing all analysis variables.

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

...

additional arguments for the lower level functions.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the 'auto' setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

time_point

(numeric)
time point at which to estimate survival.

time_unit

(string)
unit of time for the time point.

is_event

(character)
variable name storing Logical values: TRUE if event, FALSE if time to event is censored.

percent

(flag)
whether to return in percent or not.

control

(list)
relevant list of control options.

Functions

  • a_event_free(): Formatted analysis function which is used as afun.

  • s_event_free(): Statistics function which works like tern::s_surv_timepoint(), the difference is that it returns the additional statistic event_free_ci.

Examples

Run this code
adtte_f <- tern::tern_ex_adtte |>
  dplyr::filter(PARAMCD == "OS") |>
  dplyr::mutate(
    AVAL = tern::day2month(AVAL),
    is_event = CNSR == 0
  )

basic_table() |>
  split_cols_by(var = "ARMCD") |>
  analyze(
    vars = "AVAL",
    afun = a_event_free,
    show_labels = "hidden",
    na_str = tern::default_na_str(),
    extra_args = list(
      time_unit = "week",
      time_point = 3,
      is_event = "is_event"
    )
  ) |>
  build_table(df = adtte_f)
adtte_f <- tern::tern_ex_adtte |>
  dplyr::filter(PARAMCD == "OS") |>
  dplyr::mutate(
    AVAL = tern::day2month(AVAL),
    is_event = CNSR == 0
  )

s_event_free(
  df = adtte_f,
  .var = "AVAL",
  time_point = 6,
  is_event = "is_event",
  time_unit = "month"
)

Run the code above in your browser using DataLab