Filter Hierarchical Tables

This function is used to filter hierarchical table rows. Filters are not applied to summary or overall rows.

Usage

filter_hierarchical(x, ...)

# S3 method for class 'tbl_hierarchical'
filter_hierarchical(
  x,
  filter,
  var = NULL,
  keep_empty = FALSE,
  quiet = FALSE,
  ...
)

# S3 method for class 'tbl_hierarchical_count'
filter_hierarchical(
  x,
  filter,
  var = NULL,
  keep_empty = FALSE,
  quiet = FALSE,
  ...
)

# S3 method for class 'tbl_ard_hierarchical'
filter_hierarchical(
  x,
  filter,
  var = NULL,
  keep_empty = FALSE,
  quiet = FALSE,
  ...
)

Arguments

x: (tbl_hierarchical, tbl_hierarchical_count, tbl_ard_hierarchical)
A hierarchical gtsummary table of class 'tbl_hierarchical', 'tbl_hierarchical_count', or 'tbl_ard_hierarchical'.
...: These dots are for future extensions and must be empty.
filter: (expression)
An expression that is used to filter rows of the table. See the Details section below.
var: (tidy-select)
Hierarchy variable from x to perform filtering on. The variable must be present in x$inputs$include. If NULL, the last hierarchy variable from x (dplyr::last(x$inputs$include)) will be used.
keep_empty: (scalar logical)
Logical argument indicating whether to retain summary rows corresponding to table hierarchy sections that have had all rows filtered out. Default is FALSE.
quiet: (logical)
Logical indicating whether to suppress any messaging. Default is FALSE.

Value

a gtsummary table of the same class as x.

Details

The filter argument can be used to filter out rows of a table which do not meet the criteria provided as an expression. Rows can be filtered on the values of any of the possible statistics (n, p, and N) provided they are included at least once in the table, as well as the values of any by variables.

Additionally, filters can be applied on individual column values (if a by variable was specified) via the n_XX, N_XX, and p_XX statistics, where each XX represents the index of the column to select the statistic from. For example, filter = n_1 > 5 will check whether n values in the first column of the table are greater than 5 in each row.

Overall statistics for each row can be used in filters via the n_overall, N_overall, and p_overall statistics. If used in filter, overall statistics are derived within the filtering function. n_overall can only be derived if n statistic is present in the table for the filter variable, N_overall if the N statistic is present for the filter variable, and p_overall if both the n and N statistics are present for the filter variable.

By default, filters will be applied at the level of the innermost hierarchy variable, i.e. the last variable supplied to variables. If filters should instead be applied at the level of one of the outer hierarchy variables, the var parameter can be used to specify a different variable to filter on. When var is set to a different (outer) variable and a level of the variable does not meet the filtering criteria then the section corresponding to that variable level - including summary rows - and all sub-sections within that section will be removed.

If an overall column was added to the table (via add_overall())) this column will not be used in any filters (i.e. n_overall will not include the overall n in a given row).

Some examples of possible filters:

filter = n > 5: keep rows where one of the treatment groups observed more than 5 AEs
filter = n == 2 & p < 0.05: keep rows where one of the treatment groups observed exactly 2 AEs and one of the treatment groups observed a proportion less than 5%.
filter = n_overall >= 4: keep rows where there were 4 or more AEs observed across the row
filter = mean(n) > 4 | n > 3: keep rows where the mean number of AEs is 4 or more across the row or one of the treatment groups observed more than 3 AEs
filter = n_2 > 2: keep rows where the "Xanomeline High Dose" treatment group observed more than 2 AEs

Examples

ADAE_subset <- cards::ADAE |>
  dplyr::filter(AEBODSYS %in% c("SKIN AND SUBCUTANEOUS TISSUE DISORDERS",
                                "EAR AND LABYRINTH DISORDERS")) |>
  dplyr::filter(.by = AEBODSYS, dplyr::row_number() < 20)

tbl <-
  tbl_hierarchical(
    data = ADAE_subset,
    variables = c(AEBODSYS, AEDECOD),
    by = TRTA,
    denominator = cards::ADSL,
    id = USUBJID,
    overall_row = TRUE
  )

# Example 1 ----------------------------------
# Keep rows where less than 2 AEs are observed across the row
filter_hierarchical(tbl, sum(n) < 2)


  Body System or Organ Class

    Dictionary-Derived Term
      Placebo

N = 86¹
      Xanomeline High Dose

N = 84¹
      Xanomeline Low Dose

N = 84¹
    
Number of patients with event
3 (3.5%)
3 (3.6%)
5 (6.0%)
EAR AND LABYRINTH DISORDERS
1 (1.2%)
1 (1.2%)
2 (2.4%)
    CERUMEN IMPACTION
0 (0%)
0 (0%)
1 (1.2%)
    EAR PAIN
1 (1.2%)
0 (0%)
0 (0%)
    TINNITUS
0 (0%)
0 (0%)
1 (1.2%)
SKIN AND SUBCUTANEOUS TISSUE DISORDERS
2 (2.3%)
2 (2.4%)
3 (3.6%)
    ACTINIC KERATOSIS
0 (0%)
1 (1.2%)
0 (0%)
    PRURITUS GENERALISED
0 (0%)
0 (0%)
1 (1.2%)
    RASH
0 (0%)
1 (1.2%)
0 (0%)
¹ n (%)
    

# Example 2 ----------------------------------
# Keep rows where at least one treatment group in the row has at least 2 AEs observed
filter_hierarchical(tbl, n >= 2)


  Body System or Organ Class

    Dictionary-Derived Term
      Placebo

N = 86¹
      Xanomeline High Dose

N = 84¹
      Xanomeline Low Dose

N = 84¹
    
Number of patients with event
3 (3.5%)
3 (3.6%)
5 (6.0%)
SKIN AND SUBCUTANEOUS TISSUE DISORDERS
2 (2.3%)
2 (2.4%)
3 (3.6%)
    ERYTHEMA
1 (1.2%)
0 (0%)
3 (3.6%)
    PRURITUS
1 (1.2%)
0 (0%)
2 (2.4%)
¹ n (%)
    

# Example 3 ----------------------------------
# Keep rows where AEs across the row have an overall prevalence of greater than 0.5%
filter_hierarchical(tbl, p_overall > 0.005)


  Body System or Organ Class

    Dictionary-Derived Term
      Placebo

N = 86¹
      Xanomeline High Dose

N = 84¹
      Xanomeline Low Dose

N = 84¹
    
Number of patients with event
3 (3.5%)
3 (3.6%)
5 (6.0%)
EAR AND LABYRINTH DISORDERS
1 (1.2%)
1 (1.2%)
2 (2.4%)
    VERTIGO
0 (0%)
1 (1.2%)
1 (1.2%)
SKIN AND SUBCUTANEOUS TISSUE DISORDERS
2 (2.3%)
2 (2.4%)
3 (3.6%)
    ERYTHEMA
1 (1.2%)
0 (0%)
3 (3.6%)
    PRURITUS
1 (1.2%)
0 (0%)
2 (2.4%)
¹ n (%)
    

# Example 4 ----------------------------------
# Keep rows where SOCs across the row have an overall prevalence of greater than 20
filter_hierarchical(tbl, n_overall > 20, var = AEBODSYS)


  Body System or Organ Class

    Dictionary-Derived Term
      Placebo

N = 86¹
      Xanomeline High Dose

N = 84¹
      Xanomeline Low Dose

N = 84¹
    
Number of patients with event
3 (3.5%)
3 (3.6%)
5 (6.0%)
¹ n (%)
    

# Example 5 ----------------------------------
# Keep AEs that have a difference in prevalence of greater than 3% between reference group with
# `TRTA = "Xanomeline High Dose"` and comparison group with `TRTA = "Xanomeline Low Dose"`
filter_hierarchical(tbl, abs(p_2 - p_3) > 0.03)
#> When applying filters on specific levels of `by` variable "TRTA" xx_1 =
#> "Placebo", xx_2 = "Xanomeline High Dose", and xx_3 = "Xanomeline Low Dose".


  Body System or Organ Class

    Dictionary-Derived Term
      Placebo

N = 86¹
      Xanomeline High Dose

N = 84¹
      Xanomeline Low Dose

N = 84¹
    
Number of patients with event
3 (3.5%)
3 (3.6%)
5 (6.0%)
SKIN AND SUBCUTANEOUS TISSUE DISORDERS
2 (2.3%)
2 (2.4%)
3 (3.6%)
    ERYTHEMA
1 (1.2%)
0 (0%)
3 (3.6%)
¹ n (%)

Body System or Organ Class Dictionary-Derived Term	Placebo N = 86¹	Xanomeline High Dose N = 84¹	Xanomeline Low Dose N = 84¹
Number of patients with event	3 (3.5%)	3 (3.6%)	5 (6.0%)
EAR AND LABYRINTH DISORDERS	1 (1.2%)	1 (1.2%)	2 (2.4%)
CERUMEN IMPACTION	0 (0%)	0 (0%)	1 (1.2%)
EAR PAIN	1 (1.2%)	0 (0%)	0 (0%)
TINNITUS	0 (0%)	0 (0%)	1 (1.2%)
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	2 (2.3%)	2 (2.4%)	3 (3.6%)
ACTINIC KERATOSIS	0 (0%)	1 (1.2%)	0 (0%)
PRURITUS GENERALISED	0 (0%)	0 (0%)	1 (1.2%)
RASH	0 (0%)	1 (1.2%)	0 (0%)
¹ n (%)

Usage

Arguments

Value

Details

See also

Examples