This module produces a multi-variable logistic regression table consistent with the TLG Catalog template
LGRT02
available here.
Usage
tm_t_logistic(
label,
dataname,
parentname = ifelse(inherits(arm_var, "data_extract_spec"),
teal.transform::datanames_input(arm_var), "ADSL"),
arm_var = NULL,
arm_ref_comp = NULL,
paramcd,
cov_var = NULL,
avalc_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
"AVALC"), "AVALC", fixed = 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()
)
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 toADSL
.- arm_var
(
teal.transform::choices_selected()
orNULL
)
object with all available choices and preselected option for variable names that can be used asarm_var
. This defines the grouping variable(s) in the results table. If there are two elements selected forarm_var
, the second variable will be nested under the first variable. IfNULL
, no arm/treatment variable is included in the logistic model.- arm_ref_comp
(
list
) optional,
if specified it must be a named list with each element corresponding to an arm variable inADSL
and the element must be another list (possibly with delayedteal.transform::variable_choices()
or delayedteal.transform::value_choices()
with the elements namedref
andcomp
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 fromdataname
.- cov_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for the covariates variables.- avalc_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for the analysis variable (categorical).- 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 theshiny::helpText()
elements are useful.- basic_table_args
(
basic_table_args
) optional
object created byteal.widgets::basic_table_args()
with settings for the module table. The argument is merged with optionteal.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")
.
See also
The TLG Catalog where additional example apps implementing this module can be found.
Examples
library(dplyr)
ADSL <- tmc_ex_adsl
ADRS <- tmc_ex_adrs %>%
filter(PARAMCD %in% c("BESRSPI", "INVET"))
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(
ADSL = ADSL,
ADRS = ADRS,
code = "
ADSL <- tmc_ex_adsl
ADRS <- tmc_ex_adrs %>%
filter(PARAMCD %in% c(\"BESRSPI\", \"INVET\"))
"
),
modules = modules(
tm_t_logistic(
label = "Logistic Regression",
dataname = "ADRS",
arm_var = choices_selected(
choices = variable_choices(ADRS, c("ARM", "ARMCD")),
selected = "ARM"
),
arm_ref_comp = arm_ref_comp,
paramcd = choices_selected(
choices = value_choices(ADRS, "PARAMCD", "PARAM"),
selected = "BESRSPI"
),
cov_var = choices_selected(
choices = c("SEX", "AGE", "BMRKR1", "BMRKR2"),
selected = "SEX"
)
)
)
)
#> Initializing tm_t_logistic
if (interactive()) {
shinyApp(app$ui, app$server)
}