Teal Module: Cox Regression Model

tm_t_coxreg(
  label,
  dataname,
  parentname = ifelse(inherits(arm_var, "data_extract_spec"),
    teal.transform::datanames_input(arm_var), "ADSL"),
  arm_var,
  arm_ref_comp = NULL,
  paramcd,
  cov_var,
  strata_var,
  aval_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "AVAL"), "AVAL", fixed = TRUE),
  cnsr_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
    "CNSR"), "CNSR", fixed = TRUE),
  multivariate = TRUE,
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  pre_output = NULL,
  post_output = NULL,
  basic_table_args = teal.widgets::basic_table_args()
)

## First example
## =============
## The example below is based on the usual approach involving creation of
## a random CDISC dataset and then running the application.

adsl <- tmc_ex_adsl
adtte <- tmc_ex_adtte
arm_ref_comp <- list(
  ACTARMCD = 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("ADTTE", adtte)
  ),
  modules = modules(
    tm_t_coxreg(
      label = "Cox Reg.",
      dataname = "ADTTE",
      arm_var = choices_selected(c("ARM", "ARMCD", "ACTARMCD"), "ARM"),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(adtte, "PARAMCD", "PARAM"), "OS"
      ),
      strata_var = choices_selected(
        c("COUNTRY", "STRATA1", "STRATA2"), "STRATA1"
      ),
      cov_var = choices_selected(
        c("AGE", "BMRKR1", "BMRKR2", "REGION1"), "AGE"
      ),
      multivariate = TRUE
    )
  )
)
#> [INFO] 2023-08-14 14:44:15.0555 pid:1183 token:[] teal.modules.clinical Initializing tm_t_coxreg
if (interactive()) {
  shinyApp(app$ui, app$server)
}


## Second example
## ==============
## This time, a synthetic pair of ADTTE/ADSL is fabricated for a Cox regression
## where ties and pval_method matter.

## Dataset fabrication
## -------------------

adtte <- data.frame(
  STUDYID = "LUNG",
  AVAL = c(4, 3, 1, 1, 2, 2, 3, 1, 2),
  CNSR = c(1, 1, 1, 0, 1, 1, 0, 0, 0),
  ARMCD = factor(
    c(0, 1, 1, 1, 1, 0, 0, 0, 0),
    labels = c("ARM A", "ARM B")
  ),
  SEX = factor(
    c(0, 0, 0, 0, 1, 1, 1, 1, 1),
    labels = c("F", "M")
  ),
  INST = factor(c("A", "A", "B", "B", "A", "B", "A", "B", "A")),
  stringsAsFactors = FALSE
)
adtte <- base::rbind(adtte, adtte, adtte, adtte)
adtte <- dplyr::as_tibble(adtte)
set.seed(1)
adtte$INST <- sample(adtte$INST)
adtte$AGE <- sample(seq(5, 75, 5), size = nrow(adtte), replace = TRUE)
adtte$USUBJID <- paste("sub", 1:nrow(adtte), adtte$INST, sep = "-")
adtte$PARAM <- adtte$PARAMCD <- "OS"
adsl <- subset(
  adtte,
  select = c("USUBJID", "STUDYID", "ARMCD", "SEX", "INST", "AGE")
)

## Teal application
## ================
## Note that the R code exported by `Show R Code` does not include the data
## preprocessing. You will need to create the dataset as above before
## running the exported R code.

arm_ref_comp <- list(ARMCD = list(ref = "ARM A", comp = c("ARM B")))
app <- init(
  data = cdisc_data(
    cdisc_dataset(
      dataname = "ADSL",
      x = adsl
    ),
    cdisc_dataset(
      dataname = "ADTTE",
      x = adtte
    )
  ),
  modules = modules(
    tm_t_coxreg(
      label = "Cox Reg.",
      dataname = "ADTTE",
      arm_var = choices_selected(c("ARMCD"), "ARMCD"),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(adtte, "PARAMCD", "PARAM"), "OS"
      ),
      strata_var = choices_selected(c("INST"), NULL),
      cov_var = choices_selected(c("SEX", "AGE"), "SEX"),
      multivariate = TRUE
    )
  )
)
#> [INFO] 2023-08-14 14:44:15.2517 pid:1183 token:[] teal.modules.clinical Initializing tm_t_coxreg
if (interactive()) {
  shinyApp(app$ui, app$server)
}