tm_t_abnormality: teal Module: Abnormality Summary Table

In teal.modules.clinical: 'teal' Modules for Standard Clinical Outputs

teal Module: Abnormality Summary Table

Description

This module produces a table to summarize abnormality.

Usage

tm_t_abnormality(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  arm_var,
  by_vars,
  grade,
  abnormal = list(low = c("LOW", "LOW LOW"), high = c("HIGH", "HIGH HIGH")),
  id_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    subset = "USUBJID"), selected = "USUBJID", fixed = TRUE),
  baseline_var =
    teal.transform::choices_selected(teal.transform::variable_choices(dataname, subset =
    "BNRIND"), selected = "BNRIND", fixed = TRUE),
  treatment_flag_var =
    teal.transform::choices_selected(teal.transform::variable_choices(dataname, subset =
    "ONTRTFL"), selected = "ONTRTFL", fixed = TRUE),
  treatment_flag = teal.transform::choices_selected("Y"),
  add_total = TRUE,
  total_label = default_total_label(),
  exclude_base_abn = FALSE,
  drop_arm_levels = TRUE,
  pre_output = NULL,
  post_output = NULL,
  na_level = default_na_str(),
  basic_table_args = teal.widgets::basic_table_args(),
  transformators = list(),
  decorators = list()
)

Arguments

label	(character) menu item label of the module in the teal app.
dataname	(character) analysis data used in teal module.
parentname	(character) parent analysis data used in teal module, usually this refers to ADSL.
arm_var	(teal.transform::choices_selected()) object with all available choices and preselected option for variable names that can be used as arm_var. It defines the grouping variable in the results table.
by_vars	(teal.transform::choices_selected()) object with all available choices and preselected option for variable names used to split the summary by rows.
grade	(teal.transform::choices_selected()) object with all available choices and preselected option for variable names that can be used to specify the abnormality grade. Variable must be factor.
abnormal	(⁠named list⁠) defined by user to indicate what abnormalities are to be displayed.
id_var	(teal.transform::choices_selected()) object specifying the variable name for subject id.
baseline_var	(teal.transform::choices_selected()) variable for baseline abnormality grade.
treatment_flag_var	(teal.transform::choices_selected()) on treatment flag variable.
treatment_flag	(teal.transform::choices_selected()) value indicating on treatment records in treatment_flag_var.
add_total	(logical) whether to include column with total number of patients.
total_label	(string) string to display as total column/row label if column/row is enabled (see add_total). Defaults to "All Patients". To set a new default total_label to apply in all modules, run set_default_total_label("new_default").
exclude_base_abn	(logical) whether to exclude patients who had abnormal values at baseline.
drop_arm_levels	(logical) whether to drop unused levels of arm_var. If TRUE, arm_var levels are set to those used in the dataname dataset. If FALSE, arm_var levels are set to those used in the parentname dataset. If dataname and parentname are the same, then drop_arm_levels is set to TRUE and user input for this parameter is ignored.
pre_output	(shiny.tag) optional, with text placed before the output to put the output into context. For example a title.
post_output	(shiny.tag) optional, with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.
na_level	(character) the NA level in the input dataset, default to "<Missing>".
basic_table_args	(basic_table_args) optional object created by teal.widgets::basic_table_args() with settings for the module table. The argument is merged with option teal.basic_table_args and with default module arguments (hard coded in the module body). For more details, see the vignette: vignette("custom-basic-table-arguments", package = "teal.widgets").
transformators	(list of teal_transform_module) that will be applied to transform module's data input. To learn more check vignette("transform-input-data", package = "teal").
decorators	(named list of lists of teal_transform_module) optional, decorator for tables or plots included in the module output reported. The decorators are applied to the respective output objects. See section "Decorating Module" below for more details.

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• table (TableTree - output of rtables::build_table())

A Decorator is applied to the specific output using a named list of teal_transform_module objects. The name of this list corresponds to the name of the output to which the decorator is applied. See code snippet below:

tm_t_abnormality(
   ..., # arguments for module
   decorators = list(
     table = teal_transform_module(...) # applied only to `table` output
   )
)

For additional details and examples of decorators, refer to the vignette vignette("decorate-module-output", package = "teal.modules.clinical").

To learn more please refer to the vignette vignette("transform-module-output", package = "teal") or the teal::teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

Note

Patients with the same abnormality at baseline as on the treatment visit can be excluded in accordance with GDSR specifications by using exclude_base_abn.

See Also

The TLG Catalog where additional example apps implementing this module can be found.

Examples

library(dplyr)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADLB <- tmc_ex_adlb %>%
    mutate(
      ONTRTFL = case_when(
        AVISIT %in% c("SCREENING", "BASELINE") ~ "",
        TRUE ~ "Y"
      ) %>% with_label("On Treatment Record Flag")
    )
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]

app <- init(
  data = data,
  modules = modules(
    tm_t_abnormality(
      label = "Abnormality Table",
      dataname = "ADLB",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      add_total = FALSE,
      by_vars = choices_selected(
        choices = variable_choices(ADLB, subset = c("LBCAT", "PARAM", "AVISIT")),
        selected = c("LBCAT", "PARAM"),
        keep_order = TRUE
      ),
      baseline_var = choices_selected(
        variable_choices(ADLB, subset = "BNRIND"),
        selected = "BNRIND", fixed = TRUE
      ),
      grade = choices_selected(
        choices = variable_choices(ADLB, subset = "ANRIND"),
        selected = "ANRIND",
        fixed = TRUE
      ),
      abnormal = list(low = "LOW", high = "HIGH"),
      exclude_base_abn = FALSE
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

teal.modules.clinical documentation built on April 4, 2025, 12:35 a.m.