Teal Module: Teal module for Generalized Estimating Equations (GEE) analysis
Source:R/tm_a_gee.R
tm_a_gee.Rd
Teal Module: Teal module for Generalized Estimating Equations (GEE) analysis
Usage
tm_a_gee(
label,
dataname,
parentname = ifelse(inherits(arm_var, "data_extract_spec"),
teal.transform::datanames_input(arm_var), "ADSL"),
aval_var,
id_var,
arm_var,
visit_var,
cov_var,
arm_ref_comp = NULL,
paramcd,
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
.- aval_var
(
choices_selected
ordata_extract_spec
)
object with all available choices and preselected option for the analysis variable.- id_var
(
choices_selected
ordata_extract_spec
)
object specifying the variable name for subject id.- arm_var
(
choices_selected
ordata_extract_spec
)
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.- visit_var
(
choices_selected
ordata_extract_spec
)
object with all available choices and preselected option for variable names that can be used asvisit
variable. Must be a factor indataname
.- cov_var
(
choices_selected
ordata_extract_spec
)
object with all available choices and preselected option for the covariates variables.- arm_ref_comp
optional, (
list
)
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
(
choices_selected
ordata_extract_spec
)
variable value designating the studied parameter.- conf_level
(
choices_selected
)
object with all available choices and preselected 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 theshiny::helpText()
elements are useful.- basic_table_args
-
optional, (
basic_table_args
)
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")
.
Examples
adsl <- tmc_ex_adsl
adqs <- tmc_ex_adqs %>%
dplyr::filter(ABLFL != "Y" & ABLFL2 != "Y") %>%
dplyr::mutate(
AVISIT = as.factor(AVISIT),
AVISITN = rank(AVISITN) %>%
as.factor() %>%
as.numeric() %>%
as.factor(),
AVALBIN = AVAL < 50 # Just as an example to get a binary endpoint.
) %>%
droplevels()
app <- init(
data = cdisc_data(
cdisc_dataset("ADSL", adsl),
cdisc_dataset("ADQS", adqs)
),
modules = modules(
tm_a_gee(
label = "GEE",
dataname = "ADQS",
aval_var = choices_selected("AVALBIN", fixed = TRUE),
id_var = choices_selected(c("USUBJID", "SUBJID"), "USUBJID"),
arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
visit_var = choices_selected(c("AVISIT", "AVISITN"), "AVISIT"),
paramcd = choices_selected(
choices = value_choices(adqs, "PARAMCD", "PARAM"),
selected = "FKSI-FWB"
),
cov_var = choices_selected(c("BASE", "AGE", "SEX", "BASE:AVISIT"), NULL)
)
)
)
#> [INFO] 2023-08-14 14:43:58.1214 pid:1183 token:[] teal.modules.clinical Initializing tm_a_gee (prototype)
if (interactive()) {
shiny::shinyApp(app$ui, app$server)
}