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Filter Hierarchical Tables

Source: R/filter_hierarchical.R
filter_hierarchical.Rd

[Experimental]

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

Usage

filter_hierarchical(x, filter, keep_empty = FALSE)

Arguments

x

(tbl_hierarchical, tbl_hierarchical_count)
A hierarchical gtsummary table of class 'tbl_hierarchical' or 'tbl_hierarchical_count'.

filter

(expression)
An expression that is used to filter rows of the table. See the Details section below.

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.

Value

A gtsummary 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. Filtering is only applied to rows that correspond to the innermost variable in the hierarchy - all outer variable (summary) rows are kept regardless of whether they meet the filtering criteria themselves. In addition to filtering on individual statistic values, filters can be applied across the row (i.e. across all by variable values) by using aggregate functions such as sum() and mean().

If an overall column was added to the table (via add_overall())) this column will not be used in any filters (i.e. sum(n) will not include the overall n in a given row). To filter on overall statistics use the sum() function in your filter instead (i.e. sum(n) is equal to the overall column n across any by variables).

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 = sum(n) >= 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 = any(n > 2 & TRTA == "Xanomeline High Dose"): keep rows where the "Xanomeline High Dose" treatment group observed more than 2 AEs

See also

sort_hierarchical()

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 |> mutate(TRTA = ARM),
    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, sum(n) / sum(N) > 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 (%)