teal Module: Abnormality Summary Table

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()
)

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(s) in the results table. If there are two elements selected for arm_var, second variable will be nested under the first variable.

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").

Value

a teal_module object.

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

Examples

data <- teal_data()
data <- within(data, {
  library(dplyr)

  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")
    )
})
datanames <- c("ADSL", "ADLB")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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