tm_t_coxreg: teal Module: Cox Regression Model

In teal.modules.clinical: 'teal' Modules for Standard Clinical Outputs

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(),
  transformators = list(),
  decorators = list()
)

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 in the results table.
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.
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	(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").
transformators	(list of teal_transform_module) that will be applied to transform module's data input. To learn more check vignette("transform-input-data", package = "teal").
decorators	(named list of lists of teal_transform_module) optional, decorator for tables or plots included in the module output reported. The decorators are applied to the respective output objects. See section "Decorating Module" below for more details.

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.

Value

a teal_module object.

Decorating Module

This module generates the following objects, which can be modified in place using decorators:

• table (TableTree as created from rtables::build_table)

A Decorator is applied to the specific output using a named list of teal_transform_module objects. The name of this list corresponds to the name of the output to which the decorator is applied. See code snippet below:

tm_t_coxreg(
   ..., # arguments for module
   decorators = list(
     table = teal_transform_module(...) # applied only to `table` output
   )
)

For additional details and examples of decorators, refer to the vignette vignette("decorate-module-output", package = "teal.modules.clinical").

To learn more please refer to the vignette vignette("transform-module-output", package = "teal") or the teal::teal_transform_module() documentation.

Examples in Shinylive

example-1

Open in Shinylive

example-2

Open in Shinylive

Note

• The likelihood ratio test is not supported for models that include strata - the Wald test will be substituted in these cases.

• Multi-variable is the default choice for backward compatibility.

See Also

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

Examples

## 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
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]

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(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.
library(dplyr)

data <- teal_data()
data <- within(data, {
  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")
  )
})

join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]

## `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(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)
}

teal.modules.clinical documentation built on April 4, 2025, 12:35 a.m.