Pruning Function for pruning based on a fraction and/or a difference from the control arm

This is a pruning constructor function which identifies records to be pruned based on the the fraction from the percentages. In addition to just looking at a fraction within an arm this function also allows further flexibility to also prune based on a comparison versus the control arm.

Usage

bspt_pruner(
  fraction = 0.05,
  keeprowtext = "Analysis set: Safety",
  reg_expr = FALSE,
  control = NULL,
  diff_from_control = NULL,
  only_more_often = TRUE,
  cols = c("TRT01A")
)

Arguments

fraction: fraction threshold. Function will keep all records strictly greater than this threshold.
keeprowtext: Row to be excluded from pruning.
reg_expr: Apply keeprowtext as a regular expression (grepl with fixed = TRUE)
control: Control Group
diff_from_control: Difference from control threshold.
only_more_often: TRUE: Only consider when column pct is more often than control. FALSE: Also select a row where column pct is less often than control and abs(diff) above threshold
cols: column path.

Value

function that can be utilized as pruning function in prune_table

Examples

ADSL <- data.frame(
  USUBJID = c(
    'XXXXX01', 'XXXXX02', 'XXXXX03', 'XXXXX04', 'XXXXX05',
    'XXXXX06', 'XXXXX07', 'XXXXX08', 'XXXXX09', 'XXXXX10'
  ),
  TRT01P = c(
    'ARMA', 'ARMB', 'ARMA', 'ARMB', 'ARMB',
    'Placebo', 'Placebo', 'Placebo', 'ARMA', 'ARMB'
  ),
  FASFL = c('Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y'),
  SAFFL = c('N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N'),
  PKFL = c('N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N')
)

ADSL <- ADSL |>
  dplyr::mutate(TRT01P = as.factor(TRT01P)) |>
  dplyr::mutate(SAFFL = factor(SAFFL, c("Y", "N"))) |>
  dplyr::mutate(PKFL = factor(PKFL, c("Y", "N")))

lyt <- basic_table() |>
  split_cols_by('TRT01P') |>
  add_overall_col('Total') |>

  split_rows_by(
    "FASFL",
    split_fun = drop_and_remove_levels("N"),
    child_labels = "hidden"
  ) |>
  analyze('FASFL',
    var_labels = 'Analysis set:',
    afun = a_freq_j,
    show_labels = 'visible',
    extra_args = list(label = 'Full', .stats = "count_unique_fraction")
  ) |>

  split_rows_by(
    "SAFFL",
    split_fun = remove_split_levels("N"),
    child_labels = "hidden"
  ) |>
  analyze('SAFFL',
    var_labels = 'Analysis set:',
    afun = a_freq_j,
    show_labels = 'visible',
    extra_args = list(label = 'Safety', .stats = "count_unique_fraction")
  ) |>

  split_rows_by(
    "PKFL",
    split_fun = remove_split_levels("N"),
    child_labels = "hidden"
  ) |>
  analyze('PKFL',
    var_labels = 'Analysis set:',
    afun = a_freq_j,
    show_labels = 'visible',
    extra_args = list(label = 'PK', .stats = "count_unique_fraction")
  )

result <- build_table(lyt, ADSL)

result
#>                    ARMA        ARMB       Placebo       Total  
#> ———————————————————————————————————————————————————————————————
#> Analysis set:                                                  
#>   Full          3 (100.0%)   3 (75.0%)   3 (100.0%)   9 (90.0%)
#> Analysis set:                                                  
#>   Safety            0            0           0            0    
#> Analysis set:                                                  
#>   PK                0            0           0            0    

# use pruning function to prune rows where it doesn't meet 5% criteria
# in the total column but it will not
# prune the Safety row regardless
result <- prune_table(
  result,
  prune_func = bspt_pruner(
    fraction = 0.05,
    keeprowtext = 'Safety',
    cols = c('Total')
  )
)

result
#>                    ARMA        ARMB       Placebo       Total  
#> ———————————————————————————————————————————————————————————————
#> Analysis set:                                                  
#>   Full          3 (100.0%)   3 (75.0%)   3 (100.0%)   9 (90.0%)
#> Analysis set:                                                  
#>   Safety            0            0           0            0