teal Module: Laboratory test results with highest grade post-baseline
Source:R/tm_t_abnormality_by_worst_grade.R
tm_t_abnormality_by_worst_grade.Rd
This module produces a table to summarize laboratory test results with highest grade post-baseline
Usage
tm_t_abnormality_by_worst_grade(
label,
dataname,
parentname = ifelse(inherits(arm_var, "data_extract_spec"),
teal.transform::datanames_input(arm_var), "ADSL"),
arm_var,
id_var = teal.transform::choices_selected(teal.transform::variable_choices(dataname,
subset = "USUBJID"), selected = "USUBJID", fixed = TRUE),
paramcd,
atoxgr_var =
teal.transform::choices_selected(teal.transform::variable_choices(dataname, subset =
"ATOXGR"), selected = "ATOXGR", fixed = TRUE),
worst_high_flag_var =
teal.transform::choices_selected(teal.transform::variable_choices(dataname, subset =
"WGRHIFL"), selected = "WGRHIFL", fixed = TRUE),
worst_low_flag_var =
teal.transform::choices_selected(teal.transform::variable_choices(dataname, subset =
"WGRLOFL"), selected = "WGRLOFL", fixed = TRUE),
worst_flag_indicator = teal.transform::choices_selected("Y"),
add_total = TRUE,
total_label = default_total_label(),
drop_arm_levels = 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()
)
object with all available choices and preselected option for variable names that can be used asarm_var
. It defines the grouping variable(s) in the results table. If there are two elements selected forarm_var
, second variable will be nested under the first variable.- id_var
(
teal.transform::choices_selected()
)
object specifying the variable name for subject id.- paramcd
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for the parameter code variable fromdataname
.- atoxgr_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for variable names that can be used as Analysis Toxicity Grade.- worst_high_flag_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for variable names that can be used as Worst High Grade flag.- worst_low_flag_var
(
teal.transform::choices_selected()
)
object with all available choices and preselected option for variable names that can be used as Worst Low Grade flag.- worst_flag_indicator
(
teal.transform::choices_selected()
)
value indicating worst grade.- add_total
(
logical
)
whether to include column with total number of patients.- total_label
(
string
)
string to display as total column/row label if column/row is enabled (seeadd_total
). Defaults to"All Patients"
. To set a new defaulttotal_label
to apply in all modules, runset_default_total_label("new_default")
.- drop_arm_levels
(
logical
)
whether to drop unused levels ofarm_var
. IfTRUE
,arm_var
levels are set to those used in thedataname
dataset. IfFALSE
,arm_var
levels are set to those used in theparentname
dataset. Ifdataname
andparentname
are the same, thendrop_arm_levels
is set toTRUE
and user input for this parameter is ignored.- 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
ADLB <- tmc_ex_adlb %>%
filter(!AVISIT %in% c("SCREENING", "BASELINE"))
app <- init(
data = cdisc_data(
ADSL = ADSL,
ADLB = ADLB,
code = "
ADSL <- tmc_ex_adsl
ADLB <- tmc_ex_adlb %>%
filter(!AVISIT %in% c(\"SCREENING\", \"BASELINE\"))
"
),
modules = modules(
tm_t_abnormality_by_worst_grade(
label = "Laboratory Test Results with Highest Grade Post-Baseline",
dataname = "ADLB",
arm_var = choices_selected(
choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
selected = "ARM"
),
paramcd = choices_selected(
choices = value_choices(ADLB, "PARAMCD", "PARAM"),
selected = c("ALT", "CRP", "IGA")
),
add_total = FALSE
)
),
filter = teal_slices(
teal_slice("ADSL", "SAFFL", selected = "Y"),
teal_slice("ADLB", "ONTRTFL", selected = "Y")
)
)
#> Initializing tm_t_abnormality_by_worst_grade
if (interactive()) {
shinyApp(app$ui, app$server)
}