ARD Hierarchical Table

Source: R/tbl_ard_hierarchical.R

[Experimental]
This is an preview of this function. There will be changes in the coming releases, and changes will not undergo a formal deprecation cycle.

Constructs tables from nested or hierarchical data structures (e.g. adverse events).

tbl_ard_hierarchical(
  cards,
  variables,
  by = NULL,
  include = everything(),
  statistic = ~"{n} ({p}%)",
  label = NULL
)

Arguments

cards

(card)
An ARD object of class "card" typically created with cards::ard_*() functions.

variables

(tidy-select)
character vector or tidy-selector of columns in data used to create a hierarchy. Hierarchy will be built with variables in the order given.

by

(tidy-select)
a single column from data. Summary statistics will be stratified by this variable. Default is NULL.

include

(tidy-select)
variables from hierarchy for which summary statistics should be returned (on the variable label rows) Including the last element of hierarchy has no effect since each level has its own row for this variable. The default is everything().

statistic

(formula-list-selector)
used to specify the summary statistics to display for all variables in tbl_hierarchical(). The default is everything() ~ "{n} ({p})".

label

(formula-list-selector)
used to override default labels in hierarchical table, e.g. list(AESOC = "System Organ Class"). The default for each variable is the column label attribute, attr(., 'label'). If no label has been set, the column name is used.

Value

a gtsummary table of class "tbl_ard_hierarchical"

Examples

ADAE_subset <- cards::ADAE |>
  dplyr::filter(
    AESOC %in% unique(cards::ADAE$AESOC)[1:5],
    AETERM %in% unique(cards::ADAE$AETERM)[1:5]
  )

# Example 1: Event Rates  --------------------
# First, build the ARD
ard <-
  cards::ard_stack_hierarchical(
    data = ADAE_subset,
    variables = c(AESOC, AETERM),
    by = TRTA,
    denominator = cards::ADSL |> mutate(TRTA = ARM),
    id = USUBJID
  )

# Second, build table from the ARD
tbl_ard_hierarchical(
  cards = ard,
  variables = c(AESOC, AETERM),
  by = TRTA
)



  
AESOC

    AETERM		
      Placebo

N = 861		
      Xanomeline High Dose

N = 841		
      Xanomeline Low Dose

N = 841		
    
CARDIAC DISORDERS
2 (2.3%)
3 (3.6%)
0 (0.0%)
    ATRIOVENTRICULAR BLOCK SECOND DEGREE
2 (2.3%)
3 (3.6%)
0 (0.0%)
GASTROINTESTINAL DISORDERS
9 (10.5%)
4 (4.8%)
5 (6.0%)
    DIARRHOEA
9 (10.5%)
4 (4.8%)
5 (6.0%)
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
8 (9.3%)
25 (29.8%)
24 (28.6%)
    APPLICATION SITE ERYTHEMA
3 (3.5%)
15 (17.9%)
12 (14.3%)
    APPLICATION SITE PRURITUS
6 (7.0%)
22 (26.2%)
22 (26.2%)
SKIN AND SUBCUTANEOUS TISSUE DISORDERS
9 (10.5%)
14 (16.7%)
15 (17.9%)
    ERYTHEMA
9 (10.5%)
14 (16.7%)
15 (17.9%)
1 n (%)
    

# Example 2: Event Counts  -------------------
ard <-
  cards::ard_stack_hierarchical_count(
    data = ADAE_subset,
    variables = c(AESOC, AETERM),
    by = TRTA,
    denominator = cards::ADSL |> mutate(TRTA = ARM)
  )

tbl_ard_hierarchical(
  cards = ard,
  variables = c(AESOC, AETERM),
  by = TRTA,
  statistic = ~"{n}"
)



  
AESOC

    AETERM		
      Placebo

N = 861		
      Xanomeline High Dose

N = 841		
      Xanomeline Low Dose

N = 841		
    
CARDIAC DISORDERS
2
4
0
    ATRIOVENTRICULAR BLOCK SECOND DEGREE
2
4
0
GASTROINTESTINAL DISORDERS
10
4
7
    DIARRHOEA
10
4
7
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
13
58
53
    APPLICATION SITE ERYTHEMA
3
23
20
    APPLICATION SITE PRURITUS
10
35
33
SKIN AND SUBCUTANEOUS TISSUE DISORDERS
13
22
24
    ERYTHEMA
13
22
24
1 n