tm_t_coxreg: teal Module: Cox Regression Model

Description

This module fits Cox univariable or multi-variable models, consistent with the TLG Catalog templates for Cox regression tables COXT01 and COXT02, respectively. See the TLG Catalog entries for COXT01 here and COXT02 here.

Usage

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,
  na_level = default_na_str(),
  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()
)

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(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: (teal.transform::choices_selected())
object with all available choices and preselected option for the parameter code variable from dataname.
cov_var: (teal.transform::choices_selected())
object with all available choices and preselected option for the covariates variables.
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.
cnsr_var: (teal.transform::choices_selected())
object with all available choices and preselected option for the censoring variable.
multivariate: (logical)
if FALSE, the univariable approach is used instead of the multi-variable model.
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").
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).
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").

Details

The Cox Proportional Hazards (PH) model is the most commonly used method to estimate the magnitude of the effect in survival analysis. It assumes proportional hazards: the ratio of the hazards between groups (e.g., two arms) is constant over time. This ratio is referred to as the "hazard ratio" (HR) and is one of the most commonly reported metrics to describe the effect size in survival analysis.

This modules expects that the analysis data has the following variables:

AVAL: time to event
CNSR: 1 if record in AVAL is censored, 0 otherwise
PARAMCD: variable used to filter for endpoint (e.g. OS). After filtering for PARAMCD one observation per patient is expected

The arm variables and stratification/covariate variables are taken from the ADSL data.

Examples

Run this code

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

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")
  )
)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADTTE <- tmc_ex_adtte
})
datanames <- c("ADSL", "ADTTE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

app <- init(
  data = data,
  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(data[["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
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

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

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

data <- teal_data()
data <- within(data, {
  library(dplyr)
  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 <- rbind(ADTTE, ADTTE, ADTTE, ADTTE)
  ADTTE <- 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")
  )
})

datanames <- c("ADSL", "ADTTE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## `teal` application
## ----------------
## Note that the R code exported by `Show R Code` does not include the data
## pre-processing. 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 = data,
  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(data[["ADTTE"]], "PARAMCD", "PARAM"), "OS"
      ),
      strata_var = choices_selected(c("INST"), NULL),
      cov_var = choices_selected(c("SEX", "AGE"), "SEX"),
      multivariate = TRUE
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}