teal Module: Mixed Model Repeated Measurements (MMRM) Analysis

This module produces analysis tables and plots for Mixed Model Repeated Measurements.

Usage

tm_a_mmrm(
  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,
  method = teal.transform::choices_selected(c("Satterthwaite", "Kenward-Roger",
    "Kenward-Roger-Linear"), "Satterthwaite", keep_order = TRUE),
  conf_level = teal.transform::choices_selected(c(0.95, 0.9, 0.8), 0.95, keep_order =
    TRUE),
  plot_height = c(700L, 200L, 2000L),
  plot_width = NULL,
  total_label = default_total_label(),
  pre_output = NULL,
  post_output = NULL,
  basic_table_args = teal.widgets::basic_table_args(),
  ggplot2_args = teal.widgets::ggplot2_args(),
  decorators = NULL
)

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.

aval_var

(teal.transform::choices_selected())
object with all available choices and pre-selected option for the analysis variable.

id_var

(teal.transform::choices_selected())
object specifying the variable name for subject id.

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.

visit_var

(teal.transform::choices_selected())
object with all available choices and preselected option for variable names that can be used as visit variable. Must be a factor in dataname.

cov_var

(teal.transform::choices_selected())
object with all available choices and preselected option for the covariates variables.

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.

method

(teal.transform::choices_selected())
object with all available choices and pre-selected option for the adjustment method.

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).

plot_height

(numeric) optional
vector of length three with c(value, min, max). Specifies the height of the main plot and renders a slider on the plot to interactively adjust the plot height.

plot_width

(numeric) optional
vector of length three with c(value, min, max). Specifies the width of the main plot and renders a slider on the plot to interactively adjust the plot width.

total_label

(string)
string to display as total column/row label if column/row is enabled (see add_total). Defaults to "All Patients". To set a new default total_label to apply in all modules, run set_default_total_label("new_default").

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").

ggplot2_args

(ggplot2_args) optional
object created by teal.widgets::ggplot2_args() with settings for all the plots or named list of ggplot2_args objects for plot-specific settings. List names should match the following: c("default", "lsmeans", "diagnostic"). The argument is merged with option teal.ggplot2_args and with default module arguments (hard coded in the module body). For more details, see the help vignette: vignette("custom-ggplot2-arguments", package = "teal.widgets").

decorators

" (list of teal_transform_module, named list of teal_transform_module or" NULL) optional, if not NULL, decorator for tables or plots included in the module. When a named list of teal_transform_module, the decorators are applied to the respective output objects.

Otherwise, the decorators are applied to all objects, which is equivalent as using the name default.

See section "Decorating Module" below for more details.

Value

a teal_module object.

Note

The ordering of the input data sets can lead to slightly different numerical results or different convergence behavior. This is a known observation with the used package lme4. However, once convergence is achieved, the results are reliable up to numerical precision.

Decorating Module

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

lsmeans_plot (ggplot2)
diagnostic_plot (TableTree- output from rtables::build_table)
lsmeans_table (TableTree- output from rtables::build_table)
covariance_table (TableTree- output from rtables::build_table)
fixed_effects_table (TableTree- output from rtables::build_table)
diagnostic_table (TableTree- output from rtables::build_table)

Decorators can be applied to all outputs or only to specific objects using a named list of teal_transform_module objects. The "default" name is reserved for decorators that are applied to all outputs. See code snippet below:

tm_a_mrmm(
   ..., # arguments for module
   decorators = list(
     default = list(teal_transform_module(...)), # applied to all outputs
     lsmeans_plot = list(teal_transform_module(...)) # applied only to `lsmeans_plot` output
     diagnostic_plot = list(teal_transform_module(...)) # applied only to `diagnostic_plot` output
     lsmeans_table = list(teal_transform_module(...)) # applied only to `lsmeans_table` output
     covariance_table = list(teal_transform_module(...)) # applied only to `covariance_table` output
     fixed_effects_table = list(teal_transform_module(...)) # applied only to `fixed_effects_table` output
     diagnostic_table = list(teal_transform_module(...)) # applied only to `diagnostic_table` output
   )
)

Examples in Shinylive

example-1: Open in Shinylive

Examples

library(dplyr)

arm_ref_comp <- list(
  ARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  )
)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADQS <- tmc_ex_adqs %>%
    filter(ABLFL != "Y" & ABLFL2 != "Y") %>%
    filter(AVISIT %in% c("WEEK 1 DAY 8", "WEEK 2 DAY 15", "WEEK 3 DAY 22")) %>%
    mutate(
      AVISIT = as.factor(AVISIT),
      AVISITN = rank(AVISITN) %>%
        as.factor() %>%
        as.numeric() %>%
        as.factor() #' making consecutive numeric factor
    )
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

app <- init(
  data = data,
  modules = modules(
    tm_a_mmrm(
      label = "MMRM",
      dataname = "ADQS",
      aval_var = choices_selected(c("AVAL", "CHG"), "AVAL"),
      id_var = choices_selected(c("USUBJID", "SUBJID"), "USUBJID"),
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      visit_var = choices_selected(c("AVISIT", "AVISITN"), "AVISIT"),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        choices = value_choices(data[["ADQS"]], "PARAMCD", "PARAM"),
        selected = "FKSI-FWB"
      ),
      cov_var = choices_selected(c("BASE", "AGE", "SEX", "BASE:AVISIT"), NULL)
    )
  )
)
#> Initializing tm_a_mmrm
#> Initializing reporter_previewer_module
if (interactive()) {
  shinyApp(app$ui, app$server)
}