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Workaround statistics function to time point survival estimate with CI

Source: R/event_free.R
event_free.Rd

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()
)

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

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)
#>                                             ARM A               ARM B               ARM C      
#> ———————————————————————————————————————————————————————————————————————————————————————————————
#> Patients remaining at risk                   60                  65                  50        
#> Event Free Rate (%)                         92.64               89.04               86.21      
#>   Standard Error of Event Free Rate         3.17                3.66                4.53       
#>   95% CI                               (86.42, 98.85)      (81.88, 96.21)      (77.33, 95.08)  
#> 3-week event-free rate (95% CI)       0.93 (0.86, 0.99)   0.89 (0.82, 0.96)   0.86 (0.77, 0.95)
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'
)
#> $pt_at_risk
#> [1] 159
#> attr(,"label")
#> [1] "Patients remaining at risk"
#> 
#> $event_free_rate
#> [1] 82.21113
#> attr(,"label")
#> [1] "Event Free Rate (%)"
#> 
#> $rate_se
#> [1] 2.72844
#> attr(,"label")
#> [1] "Standard Error of Event Free Rate"
#> 
#> $rate_ci
#> [1] 76.86349 87.55878
#> attr(,"label")
#> [1] "95% CI"
#> 
#> $event_free_rate_3d
#> [1] 82.21113 76.86349 87.55878
#> attr(,"label")
#> [1] "Event Free Rate (95% CI)"
#> 
#> $event_free_ci
#> [1] 0.8221113 0.7686349 0.8755878
#> attr(,"label")
#> [1] "6-month event-free rate (95% CI)"
#>