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Teal Module: Adverse Events Summary

Source: R/tm_t_events_summary.R
tm_t_events_summary.Rd

Teal Module: Adverse Events Summary

Usage 

tm_t_events_summary(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  arm_var,
  flag_var_anl = NULL,
  flag_var_aesi = NULL,
  dthfl_var =
    teal.transform::choices_selected(teal.transform::variable_choices(parentname,
    "DTHFL"), "DTHFL", fixed = TRUE),
  dcsreas_var =
    teal.transform::choices_selected(teal.transform::variable_choices(parentname,
    "DCSREAS"), "DCSREAS", fixed = TRUE),
  llt = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AEDECOD"), "AEDECOD", fixed = TRUE),
  aeseq_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AESEQ"), "AESEQ", fixed = TRUE),
  add_total = TRUE,
  count_subj = TRUE,
  count_pt = TRUE,
  count_events = TRUE,
  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.

flag_var_anl

(teal.transform::choices_selected() or teal.transform::data_extract_spec())
vector with names of flag variables from dataset used to count adverse event sub-groups (e.g. Serious events, Related events, etc.). Variable labels are used as table row names if they exist.

flag_var_aesi

(teal.transform::choices_selected() or teal.transform::data_extract_spec())
vector with names of flag variables from dataset used to count adverse event special interest groups. All flag variables must be of type logical. Variable labels are used as table row names if they exist.

dthfl_var

(teal.transform::choices_selected() or teal.transform::data_extract_spec())
variable for subject death flag from parentname. Records with `"Y"`` are summarized in the table row for "Total number of deaths".

dcsreas_var

(teal.transform::choices_selected() or teal.transform::data_extract_spec())
variable for study discontinuation reason from parentname. Records with "ADVERSE EVENTS" are summarized in the table row for "Total number of patients withdrawn from study due to an AE".

llt

(choices_selected or data_extract_spec)
name of the variable with low level term for events.

aeseq_var

(teal.transform::choices_selected() or teal.transform::data_extract_spec())
variable for adverse events sequence number from dataset. Used for counting total number of events.

add_total

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

count_subj

(logical)
w whether to show count of unique subjects based on USUBJID. Only applies if event flag variables are provided.

count_pt

(logical)
whether to show count of unique preferred terms based on llt. Only applies if event flag variables are provided.

count_events

(logical)
whether to show count of events based on aeseq_var. Only applies if event flag variables are provided.

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").

Examples 

library(dplyr)
library(scda)

ADSL <- synthetic_cdisc_data("latest")$adsl %>%
  mutate(
    DTHFL = case_when( # nolint
      !is.na(DTHDT) ~ "Y",
      TRUE ~ ""
    )
  )
attr(ADSL[["DTHFL"]], "label") <- "Subject Death Flag"

ADAE <- synthetic_cdisc_data("latest")$adae

add_event_flags <- function(dat) {
  dat <- dat %>%
    dplyr::mutate(
      TMPFL_SER = AESER == "Y",
      TMPFL_REL = AEREL == "Y",
      TMPFL_GR5 = AETOXGR == "5",
      TMP_SMQ01 = !is.na(SMQ01NAM),
      TMP_SMQ02 = !is.na(SMQ02NAM),
      TMP_CQ01 = !is.na(CQ01NAM)
    )
  column_labels <- list(
    TMPFL_SER = "Serious AE",
    TMPFL_REL = "Related AE",
    TMPFL_GR5 = "Grade 5 AE",
    TMP_SMQ01 = aesi_label(dat[["SMQ01NAM"]], dat[["SMQ01SC"]]),
    TMP_SMQ02 = aesi_label("Y.9.9.9.9/Z.9.9.9.9 AESI"),
    TMP_CQ01 = aesi_label(dat[["CQ01NAM"]])
  )
  formatters::var_labels(dat)[names(column_labels)] <- as.character(column_labels)
  dat
}

# Generating user-defined event flags.
ADAE <- ADAE %>% add_event_flags()

ae_anl_vars <- names(ADAE)[startsWith(names(ADAE), "TMPFL_")]
aesi_vars <- names(ADAE)[startsWith(names(ADAE), "TMP_")]

app <- init(
  data = cdisc_data(
    cdisc_dataset("ADSL", ADSL,
      code =
        'ADSL <- synthetic_cdisc_data("latest")$adsl %>%
            mutate(
              DTHFL = case_when(  # nolint
                !is.na(DTHDT) ~ "Y",
                TRUE ~ ""
              )
            )
        attr(ADSL[["DTHFL"]], "label") <- "Subject Death Flag"
        ADSL'
    ),
    cdisc_dataset("ADAE", ADAE,
      code =
        "ADAE <- synthetic_cdisc_data('latest')$adae
        add_event_flags <- function(dat) {
          dat <- dat %>%
            dplyr::mutate(
              TMPFL_SER = AESER == 'Y',
              TMPFL_REL = AEREL == 'Y',
              TMPFL_GR5 = AETOXGR == '5',
              TMP_SMQ01 = !is.na(SMQ01NAM),
              TMP_SMQ02 = !is.na(SMQ02NAM),
              TMP_CQ01 = !is.na(CQ01NAM)
            )
          column_labels <- list(
              TMPFL_SER = 'Serious AE',
              TMPFL_REL = 'Related AE',
              TMPFL_GR5 = 'Grade 5 AE',
              TMP_SMQ01 = aesi_label(dat[['SMQ01NAM']], dat[['SMQ01SC']]),
              TMP_SMQ02 = aesi_label('Y.9.9.9.9/Z.9.9.9.9 AESI'),
              TMP_CQ01 = aesi_label(dat[['CQ01NAM']])
          )
          formatters::var_labels(dat)[names(column_labels)] <- as.character(column_labels)
          dat
        }
        # Generating user-defined event flags.
        ADAE <- ADAE %>% add_event_flags()"
    )
  ),
  modules = modules(
    tm_t_events_summary(
      label = "Adverse Events Summary",
      dataname = "ADAE",
      arm_var = choices_selected(
        choices = variable_choices("ADSL", c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      flag_var_anl = choices_selected(
        choices = variable_choices("ADAE", ae_anl_vars),
        selected = ae_anl_vars[1],
        keep_order = TRUE,
        fixed = FALSE
      ),
      flag_var_aesi = choices_selected(
        choices = variable_choices("ADAE", aesi_vars),
        selected = aesi_vars[1],
        keep_order = TRUE,
        fixed = FALSE
      ),
      add_total = TRUE
    )
  )
)
#> [INFO] 2022-10-14 09:10:46.5571 pid:3139 token:[] teal.modules.clinical Initializing tm_t_events_summary
if (FALSE) {
shinyApp(app$ui, app$server)
}