tm_t_binary_outcome: teal Module: Binary Outcome Table

Description

This module produces a binary outcome response summary table, with the option to match the template for response table RSPT01 available in the TLG Catalog here.

Usage

tm_t_binary_outcome(
  label,
  dataname,
  parentname = ifelse(test = inherits(arm_var, "data_extract_spec"), yes =
    teal.transform::datanames_input(arm_var), no = "ADSL"),
  arm_var,
  arm_ref_comp = NULL,
  paramcd,
  strata_var,
  aval_var = teal.transform::choices_selected(choices =
    teal.transform::variable_choices(dataname, c("AVALC", "SEX")), selected = "AVALC",
    fixed = FALSE),
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  default_responses = c("CR", "PR", "Y", "Complete Response (CR)",
    "Partial Response (PR)", "M"),
  rsp_table = FALSE,
  control = list(global = list(method = ifelse(rsp_table, "clopper-pearson", "waldcc"),
    conf_level = 0.95), unstrat = list(method_ci = ifelse(rsp_table, "wald", "waldcc"),
    method_test = "schouten", odds = TRUE), strat = list(method_ci = "cmh", method_test =
    "cmh")),
  add_total = FALSE,
  total_label = default_total_label(),
  na_level = default_na_str(),
  pre_output = NULL,
  post_output = NULL,
  basic_table_args = teal.widgets::basic_table_args(),
  transformators = list(),
  decorators = list()
)

Value

a teal_module object.

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.

arm_ref_comp

(list) optional,
if specified it must be a named list with each element corresponding to an arm variable in ADSL and the element must be another list (possibly with delayed teal.transform::variable_choices() or delayed teal.transform::value_choices() with the elements named ref and comp that the defined the default reference and comparison arms when the arm variable is changed.

paramcd

(teal.transform::choices_selected())
object with all available choices and preselected option for the parameter code variable from dataname.

strata_var

(teal.transform::choices_selected())
names of the variables for stratified analysis.

aval_var

(teal.transform::choices_selected())
object with all available choices and pre-selected option for the analysis variable.

conf_level

(teal.transform::choices_selected())
object with all available choices and pre-selected option for the confidence level, each within range of (0, 1).

default_responses

(list or character)
defines the default codes for the response variable in the module per value of paramcd. A passed vector is transmitted for all paramcd values. A passed list must be named and contain arrays, each name corresponding to a single value of paramcd. Each array may contain default response values or named arrays rsp of default selected response values and levels of default level choices.

rsp_table

(logical)
whether the initial set-up of the module should match RSPT01. Defaults to FALSE.

control

(named list)
named list containing 3 named lists as follows:

global: a list of settings for overall analysis with 2 named elements method and conf_level.
unstrat: a list of settings for unstratified analysis with 3 named elements method_ci and method_test, and odds. See tern::estimate_proportion_diff(), tern::test_proportion_diff(), and tern::estimate_odds_ratio(), respectively, for options and details on how these settings are implemented in the analysis.
strat: a list of settings for stratified analysis with elements method_ci and method_test. See tern::estimate_proportion_diff() and tern::test_proportion_diff(), respectively, for options and details on how these settings are implemented in the analysis.

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").

na_level

(string)
used to replace all NA or empty values in character or factor variables in the data. Defaults to "<Missing>". To set a default na_level to apply in all modules, run set_default_na_str("new_default").

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.

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.

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_binary_outcome(
   ..., # 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

Details

The display order of response categories inherits the factor level order of the source data. Use base::factor() and its levels argument to manipulate the source data in order to include/exclude or re-categorize response categories and arrange the display order. If response categories are "Missing", "Not Evaluable (NE)", or "Missing or unevaluable", 95% confidence interval will not be calculated.
Reference arms are automatically combined if multiple arms selected as reference group.

Examples

Run this code

library(dplyr)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADRS <- tmc_ex_adrs %>%
    mutate(
      AVALC = d_onco_rsp_label(AVALC) %>%
        with_label("Character Result/Finding")
    ) %>%
    filter(PARAMCD != "OVRINV" | AVISIT == "FOLLOW UP")
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADRS <- data[["ADRS"]]

arm_ref_comp <- list(
  ARMCD = list(ref = "ARM B", comp = c("ARM A", "ARM C")),
  ARM = list(ref = "B: Placebo", comp = c("A: Drug X", "C: Combination"))
)
app <- init(
  data = data,
  modules = modules(
    tm_t_binary_outcome(
      label = "Responders",
      dataname = "ADRS",
      paramcd = choices_selected(
        choices = value_choices(ADRS, "PARAMCD", "PARAM"),
        selected = "BESRSPI"
      ),
      arm_var = choices_selected(
        choices = variable_choices(ADRS, c("ARM", "ARMCD", "ACTARMCD")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      strata_var = choices_selected(
        choices = variable_choices(ADRS, c("SEX", "BMRKR2", "RACE")),
        selected = "RACE"
      ),
      default_responses = list(
        BESRSPI = list(
          rsp = c("Complete Response (CR)", "Partial Response (PR)"),
          levels = c(
            "Complete Response (CR)", "Partial Response (PR)",
            "Stable Disease (SD)", "Progressive Disease (PD)"
          )
        ),
        INVET = list(
          rsp = c("Stable Disease (SD)", "Not Evaluable (NE)"),
          levels = c(
            "Complete Response (CR)", "Not Evaluable (NE)", "Partial Response (PR)",
            "Progressive Disease (PD)", "Stable Disease (SD)"
          )
        ),
        OVRINV = list(
          rsp = c("Progressive Disease (PD)", "Stable Disease (SD)"),
          levels = c("Progressive Disease (PD)", "Stable Disease (SD)", "Not Evaluable (NE)")
        )
      )
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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