Use these functions to generate hierarchical tables.
tbl_hierarchical(): Calculates rates of events (e.g. adverse events)
utilizing the denominator and id arguments to identify the rows in data
to include in each rate calculation. If variables contains more than one
variable and the last variable in variables is an ordered factor, then
rates of events by highest level will be calculated.
tbl_hierarchical_count(): Calculates counts of events utilizing
all rows for each tabulation.
tbl_hierarchical(
data,
variables,
id,
denominator,
by = NULL,
include = everything(),
statistic = everything() ~ "{n} ({p}%)",
overall_row = FALSE,
label = NULL,
digits = NULL
)
tbl_hierarchical_count(
data,
variables,
denominator = NULL,
by = NULL,
include = everything(),
overall_row = FALSE,
statistic = everything() ~ "{n}",
label = NULL,
digits = NULL
)(data.frame)
a data frame.
(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.
(tidy-select)
argument used to subset data to identify rows in data to calculate
event rates in tbl_hierarchical().
(data.frame, integer)
used to define the denominator and enhance the output.
The argument is required for tbl_hierarchical() and optional for tbl_hierarchical_count().
The denominator argument must be specified when id is used to calculate event rates.
(tidy-select)
a single column from data. Summary statistics will be stratified by this variable.
Default is NULL.
(tidy-select)
columns from the variables argument for which summary statistics should be returned (on the variable label rows).
Including the last element of variables has no effect since each level has its own row for this variable.
The default is everything().
(formula-list-selector)
used to specify the summary statistics to display for all variables in tbl_hierarchical().
The default is everything() ~ "{n} ({p})".
(scalar logical)
whether an overall summary row should be included at the top of the table.
The default is FALSE.
(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.
(formula-list-selector)
specifies how summary statistics are rounded. Values may be either integer(s) or function(s). If not specified,
default formatting is assigned via label_style_number() for statistics n and N, and
label_style_percent(digits=1) for statistic p.
a gtsummary table of class "tbl_hierarchical" (for tbl_hierarchical()) or "tbl_hierarchical_count"
(for tbl_hierarchical_count()).
An overall row can be added to the table as the first row by specifying overall_row = TRUE. Assuming that each row
in data corresponds to one event record, this row will count the overall number of events recorded when used in
tbl_hierarchical_count(), or the overall number of patients recorded with any event when used in
tbl_hierarchical().
A label for this overall row can be specified by passing an '..ard_hierarchical_overall..' element in label.
Similarly, the rounding for statistics in the overall row can be modified using the digits argument,
again referencing the '..ard_hierarchical_overall..' name.
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 --------------------
tbl_hierarchical(
data = ADAE_subset,
variables = c(AESOC, AETERM),
by = TRTA,
denominator = cards::ADSL,
id = USUBJID,
digits = everything() ~ list(p = 1),
overall_row = TRUE,
label = list(..ard_hierarchical_overall.. = "Any Adverse Event")
)
Primary System Organ Class
Reported Term for the Adverse Event
Placebo
N = 861
Xanomeline High Dose
N = 841
Xanomeline Low Dose
N = 841
1 n (%)
# Example 2 - Rates by Highest Severity ------
tbl_hierarchical(
data = ADAE_subset |> mutate(AESEV = factor(AESEV, ordered = TRUE)),
variables = c(AESOC, AESEV),
by = TRTA,
id = USUBJID,
denominator = cards::ADSL,
include = AESEV,
label = list(AESEV = "Highest Severity")
)
#> ℹ Denominator set by "TRTA" column in `denominator` data frame.
Primary System Organ Class
Highest Severity
Placebo
N = 861
Xanomeline High Dose
N = 841
Xanomeline Low Dose
N = 841
1 n (%)
# Example 3 - Event Counts -------------------
tbl_hierarchical_count(
data = ADAE_subset,
variables = c(AESOC, AETERM, AESEV),
by = TRTA,
overall_row = TRUE,
label = list(..ard_hierarchical_overall.. = "Total Number of AEs")
)
Primary System Organ Class
Reported Term for the Adverse Event
Severity/Intensity
Placebo1
Xanomeline High Dose1
Xanomeline Low Dose1
1 n