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Teal Module: Binary Outcome Table

Source: R/tm_t_binary_outcome.R
tm_t_binary_outcome.Rd

This module produces a binary outcome response summary table, with the option to match the STREAM template RSPT01.

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,
  add_total = FALSE,
  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

(choices_selected or data_extract_spec)
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

optional, (list)
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

(choices_selected or data_extract_spec)
variable value designating the studied parameter.

strata_var

(choices_selected or data_extract_spec)
names of the variables for stratified analysis.

aval_var

(choices_selected or data_extract_spec)
object with all available choices and preselected option for the analysis variable.

conf_level

(choices_selected)
object with all available choices and preselected 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 broadcasted 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)
should the initial set-up of the module match the STREAM template rspt01. (default FALSE)

add_total

(logical)
whether to include column with total number of patients.

pre_output

optional, (shiny.tag)
with text placed before the output to put the output into context. For example a title.

post_output

optional, (shiny.tag)
with text placed after the output to put the output into context. For example the shiny::helpText() elements are useful.

basic_table_args

optional, (basic_table_args)
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

an teal::module() object

Details

Additional standard UI inputs include responders, ref_arm, comp_arm and combine_comp_arms (default FALSE)

Default values of the inputs var_arm, ref_arm and comp_arm are set to NULL, and updated accordingly based on selection of paramcd and var_arm

This display order of response categories in partitioned statistics section 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" or "Not Evaluable (NE)" or "Missing or unevaluable", 95\ confidence interval will not be calculated.

Reference arms automatically combined if multiple arms selected as reference group.

Examples 

library(dplyr)
library(scda)

synthetic_cdisc_data_latest <- synthetic_cdisc_data("latest")
ADSL <- synthetic_cdisc_data_latest$adsl
ADRS <- synthetic_cdisc_data_latest$adrs %>%
  mutate(AVALC = d_onco_rsp_label(AVALC)) %>%
  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(
    cdisc_dataset("ADSL", ADSL),
    cdisc_dataset("ADRS", ADRS),
    code =
      "synthetic_cdisc_data_latest <- synthetic_cdisc_data('latest')
       ADSL <- synthetic_cdisc_data_latest$adsl
       ADRS <- synthetic_cdisc_data_latest$adrs %>%
       mutate(AVALC = d_onco_rsp_label(AVALC)) %>%
       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")),
        select = "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)")
        )
      )
    )
  )
)
#> [INFO] 2022-10-14 09:10:26.3373 pid:3139 token:[] teal.modules.clinical Initializing tm_t_binary_outcome
if (FALSE) {
shinyApp(app$ui, app$server)
}