teal Module: Binary Outcome Table

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()
)

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.

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").

Value

a teal_module object.

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.

See also

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

Examples

library(dplyr)

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")

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 = cdisc_data(
    ADSL = ADSL,
    ADRS = ADRS,
    code = "
      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\")
    "
  ),
  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)")
        )
      )
    )
  )
)
#> Initializing tm_t_binary_outcome
if (interactive()) {
  shinyApp(app$ui, app$server)
}