/
tm_a_mmrm.R
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tm_a_mmrm.R
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#' Template: Mixed Model Repeated Measurements (MMRM) Analysis
#'
#' Creates a valid expression to generate analysis tables and plots for Mixed Model Repeated Measurements.
#'
#' @inheritParams template_arguments
#' @param method (`string`)\cr a string specifying the adjustment method.
#' @param cor_struct (`string`)\cr a string specifying the correlation structure, defaults to
#' `"unstructured"`. See [tern.mmrm::build_formula()] for more options.
#' @param weights_emmeans argument from [emmeans::emmeans()], "proportional" by default.
#' @param parallel (`flag`)\cr flag that controls whether optimizer search can use available free cores on the
#' machine (not default).
#'
#' @inherit template_arguments return
#'
#' @seealso [tm_a_mmrm()]
#'
#' @keywords internal
template_fit_mmrm <- function(parentname,
dataname,
aval_var,
arm_var,
ref_arm,
comp_arm = NULL,
combine_comp_arms = FALSE,
id_var,
visit_var,
cov_var,
conf_level = 0.95,
method = "Satterthwaite",
cor_struct = "unstructured",
weights_emmeans = "proportional",
parallel = FALSE) {
# Data
y <- list()
data_list <- list()
parent_list <- list()
if (!is.null(arm_var)) {
ref_arm_val <- paste(ref_arm, collapse = "/")
data_list <- add_expr(
data_list,
prepare_arm(
dataname = dataname,
arm_var = arm_var,
ref_arm = ref_arm,
comp_arm = comp_arm,
ref_arm_val = ref_arm_val
)
)
parent_list <- add_expr(
parent_list,
prepare_arm(
dataname = parentname,
arm_var = arm_var,
ref_arm = ref_arm,
comp_arm = comp_arm,
ref_arm_val = ref_arm_val
)
)
if (combine_comp_arms) {
data_list <- add_expr(
data_list,
substitute_names(
expr = dplyr::mutate(arm_var = combine_levels(arm_var, levels = comp_arm)),
names = list(arm_var = as.name(arm_var)),
others = list(comp_arm = comp_arm)
)
)
parent_list <- add_expr(
parent_list,
substitute_names(
expr = dplyr::mutate(arm_var = combine_levels(arm_var, levels = comp_arm)),
names = list(arm_var = as.name(arm_var)),
others = list(comp_arm = comp_arm)
)
)
}
} else {
data_list <- add_expr(
data_list,
substitute(
expr = dataname,
env = list(
dataname = as.name(dataname)
)
)
)
parent_list <- add_expr(
parent_list,
substitute(
expr = parentname,
env = list(
parentname = as.name(parentname)
)
)
)
}
data_list <- add_expr(data_list, quote(df_explicit_na(na_level = default_na_str())))
parent_list <- add_expr(parent_list, quote(df_explicit_na(na_level = default_na_str())))
y$data <- substitute(
expr = {
anl <- data_pipe
parentname <- parent_pipe
},
env = list(
data_pipe = pipe_expr(data_list),
parentname = as.name(parentname),
parent_pipe = pipe_expr(parent_list)
)
)
vars <- substitute(
expr = list(
response = aval_var,
covariates = cov_var,
id = id_var,
arm = arm_var,
visit = visit_var
),
env = list(
aval_var = aval_var,
cov_var = cov_var,
id_var = id_var,
arm_var = arm_var,
visit_var = visit_var
)
)
y$fit <- substitute(
expr = fit <- tern.mmrm::fit_mmrm(
vars = vars,
data = anl,
conf_level = conf_level,
method = method,
cor_struct = cor_struct,
weights_emmeans = weights_emmeans,
parallel = parallel
),
env = list(
vars = vars,
conf_level = conf_level,
method = method,
cor_struct = cor_struct,
weights_emmeans = weights_emmeans,
parallel = parallel
)
)
y
}
#' @describeIn template_fit_mmrm Creates valid expressions to generate MMRM LS means, covariance matrix,
#' fixed effects, and diagnostic tables.
#'
#' @inheritParams template_arguments
#' @param fit_name (`string`)\cr name of fitted MMRM object.
#' @param show_relative (`string`)\cr should the "reduction" (`control - treatment`, default) or the "increase"
#' (`treatment - control`) be shown for the relative change from baseline.
#' @param table_type (`string`)\cr type of table to output.
#'
#' @keywords internal
template_mmrm_tables <- function(parentname,
dataname,
fit_name,
arm_var,
ref_arm,
visit_var,
paramcd,
show_relative = c("increase", "reduction", "none"),
table_type = "t_mmrm_cov",
total_label = default_total_label(),
basic_table_args = teal.widgets::basic_table_args()) {
y <- list()
ref_arm_val <- paste(ref_arm, collapse = "/")
all_basic_table_args <- teal.widgets::resolve_basic_table_args(basic_table_args)
# Build layout.
layout_list <- list()
layout_list <- layout_list %>%
add_expr(substitute(
expr = expr_basic_table_args,
env = list(expr_basic_table_args = teal.widgets::parse_basic_table_args(all_basic_table_args))
))
if (!is.null(arm_var)) {
layout_list <- add_expr(
layout_list,
substitute(
expr = rtables::split_cols_by(var = arm_var, ref_group = ref_arm),
env = list(arm_var = arm_var, ref_arm = ref_arm_val)
)
)
show_relative <- match.arg(show_relative)
if (show_relative == "none") {
layout_list <- add_expr(
layout_list,
substitute(
expr = rtables::add_colcounts() %>%
rtables::split_rows_by(visit_var) %>%
append_varlabels(dataname, visit_var) %>%
tern.mmrm::summarize_lsmeans(
.stats = c(
"n",
"adj_mean_se",
"adj_mean_ci",
"diff_mean_se",
"diff_mean_ci",
"p_value"
)
) %>%
rtables::append_topleft(paste0(" ", paramcd)),
env = list(
dataname = as.name(dataname),
visit_var = visit_var,
paramcd = paramcd
)
)
)
} else {
layout_list <- add_expr(
layout_list,
substitute(
expr = rtables::add_colcounts() %>%
rtables::split_rows_by(visit_var) %>%
append_varlabels(dataname, visit_var) %>%
tern.mmrm::summarize_lsmeans(show_relative = show_relative) %>%
rtables::append_topleft(paste0(" ", paramcd)),
env = list(
dataname = as.name(dataname),
visit_var = visit_var,
paramcd = paramcd,
show_relative = show_relative
)
)
)
}
} else {
layout_list <- add_expr(
layout_list,
substitute(
expr = rtables::add_overall_col(total_label) %>%
rtables::split_rows_by(visit_var) %>%
tern.mmrm::summarize_lsmeans(arms = FALSE) %>%
rtables::append_topleft(paste0(" ", paramcd)),
env = list(
total_label = total_label,
visit_var = visit_var,
paramcd = paramcd
)
)
)
}
y$layout <- substitute(
expr = lyt <- layout_pipe,
env = list(layout_pipe = pipe_expr(layout_list))
)
switch(table_type,
t_mmrm_lsmeans = {
y$lsmeans_table <- substitute(
expr = {
lsmeans_table <- rtables::build_table(
lyt = lyt,
df = df_explicit_na(broom::tidy(fit_mmrm), na_level = default_na_str()),
alt_counts_df = parentname
)
lsmeans_table
},
env = list(
parentname = as.name(parentname),
fit_mmrm = as.name(fit_name)
)
)
},
t_mmrm_cov = {
y$cov_matrix <- substitute(
expr = {
cov_matrix <- tern.mmrm::as.rtable(fit_mmrm, type = "cov")
subtitles(cov_matrix) <- st
cov_matrix
},
env = list(
fit_mmrm = as.name(fit_name),
st = basic_table_args$subtitles
)
)
},
t_mmrm_fixed = {
y$fixed_effects <- substitute(
expr = {
fixed_effects <- tern.mmrm::as.rtable(fit_mmrm, type = "fixed")
subtitles(fixed_effects) <- st
fixed_effects
},
env = list(
fit_mmrm = as.name(fit_name),
st = basic_table_args$subtitles
)
)
},
t_mmrm_diagnostic = {
y$diagnostic_table <- substitute(
expr = {
diagnostic_table <- tern.mmrm::as.rtable(fit_mmrm, type = "diagnostic")
subtitles(diagnostic_table) <- st
diagnostic_table
},
env = list(
fit_mmrm = as.name(fit_name),
st = basic_table_args$subtitles
)
)
}
)
y
}
#' @describeIn template_fit_mmrm Creates valid expressions to generate MMRM LS means and
#' diagnostic plots.
#'
#' @inheritParams template_arguments
#' @param lsmeans_plot (named `list`)\cr a `list` of controls for LS means plot.
#' See more [tern.mmrm::g_mmrm_lsmeans()].
#' @param diagnostic_plot (named `list`)\cr a `list` of controls for diagnostic_plot.
#' See more [tern.mmrm::g_mmrm_diagnostic()].
#'
#' @keywords internal
template_mmrm_plots <- function(fit_name,
lsmeans_plot = list(
select = c("estimates", "contrasts"),
width = 0.6,
show_pval = FALSE
),
diagnostic_plot = list(
type = "fit-residual",
z_threshold = NULL
),
ggplot2_args = teal.widgets::ggplot2_args()) {
y <- list()
if (!is.null(lsmeans_plot)) {
parsed_ggplot2_args <- teal.widgets::parse_ggplot2_args(
teal.widgets::resolve_ggplot2_args(
user_plot = ggplot2_args[["lsmeans"]],
user_default = ggplot2_args[["default"]]
)
)
plot_call <- substitute(
expr =
tern.mmrm::g_mmrm_lsmeans(
fit_mmrm,
select = select,
width = width,
show_pval = show_pval,
titles = if (is.null(fit_mmrm$vars$arm)) {
c(
estimates = paste("Adjusted mean of", fit_mmrm$labels$response, " at visits"),
contrasts = " "
)
} else {
c(
estimates = paste(
"Adjusted mean of",
fit_mmrm$labels$response,
"by treatment at visits"
),
contrasts = paste0(
"Differences of ",
fit_mmrm$labels$response,
" adjusted means vs. control ('",
fit_mmrm$ref_level,
"')"
)
)
}
),
env = list(
fit_mmrm = as.name(fit_name),
select = lsmeans_plot$select,
width = lsmeans_plot$width,
show_pval = lsmeans_plot$show_pval
)
)
y$lsmeans_plot <- substitute(
expr = {
lsmeans_plot <- plot_call
lsmeans_plot
},
env = list(
plot_call = Reduce(function(x, y) call("+", x, y), c(plot_call, parsed_ggplot2_args))
)
)
}
if (!is.null(diagnostic_plot)) {
parsed_ggplot2_args <- teal.widgets::parse_ggplot2_args(
teal.widgets::resolve_ggplot2_args(
user_plot = ggplot2_args[["diagnostic"]],
user_default = ggplot2_args[["default"]]
)
)
plot_call <- substitute(
expr =
tern.mmrm::g_mmrm_diagnostic(
fit_mmrm,
type = type,
z_threshold = z_threshold
),
env = list(
fit_mmrm = as.name(fit_name),
type = diagnostic_plot$type,
z_threshold = diagnostic_plot$z_threshold
)
)
y$diagnostic_plot <- substitute(
expr = {
diagnostic_plot <- plot_call
diagnostic_plot
},
env = list(
plot_call = Reduce(function(x, y) call("+", x, y), c(plot_call, parsed_ggplot2_args))
)
)
}
y
}
#' teal Module: Mixed Model Repeated Measurements (MMRM) Analysis
#'
#' This module produces analysis tables and plots for Mixed Model Repeated Measurements.
#'
#' @inheritParams module_arguments
#' @inheritParams template_mmrm_tables
#' @inheritParams template_mmrm_plots
#' @param method ([teal.transform::choices_selected()])\cr object with
#' all available choices and pre-selected option for the adjustment method.
#' @param ggplot2_args (`ggplot2_args`) optional\cr 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")`.
#'
#' @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.
#'
#' @inherit module_arguments return seealso
#'
#' @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
#' )
#' })
#'
#' datanames <- c("ADSL", "ADQS")
#' datanames(data) <- datanames
#' join_keys(data) <- default_cdisc_join_keys[datanames]
#' 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)
#' )
#' )
#' )
#' if (interactive()) {
#' shinyApp(app$ui, app$server)
#' }
#'
#' @export
tm_a_mmrm <- function(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()) {
message("Initializing tm_a_mmrm")
cov_var <- teal.transform::add_no_selected_choices(cov_var, multiple = TRUE)
checkmate::assert_string(label)
checkmate::assert_string(total_label)
checkmate::assert_string(dataname)
checkmate::assert_class(aval_var, "choices_selected")
checkmate::assert_class(id_var, "choices_selected")
checkmate::assert_class(arm_var, "choices_selected")
checkmate::assert_class(visit_var, "choices_selected")
checkmate::assert_class(cov_var, "choices_selected")
checkmate::assert_class(paramcd, "choices_selected")
checkmate::assert_class(method, "choices_selected")
checkmate::assert_class(conf_level, "choices_selected")
checkmate::assert_numeric(plot_height, len = 3, any.missing = FALSE, finite = TRUE)
checkmate::assert_numeric(plot_height[1], lower = plot_height[2], upper = plot_height[3], .var.name = "plot_height")
checkmate::assert_numeric(plot_width, len = 3, any.missing = FALSE, null.ok = TRUE, finite = TRUE)
checkmate::assert_numeric(
plot_width[1],
lower = plot_width[2], upper = plot_width[3], null.ok = TRUE, .var.name = "plot_width"
)
checkmate::assert_class(pre_output, classes = "shiny.tag", null.ok = TRUE)
checkmate::assert_class(post_output, classes = "shiny.tag", null.ok = TRUE)
checkmate::assert_class(basic_table_args, "basic_table_args")
if (inherits(ggplot2_args, "ggplot2_args")) ggplot2_args <- list(default = ggplot2_args)
plot_choices <- c("lsmeans", "diagnostic")
checkmate::assert_list(ggplot2_args, types = "ggplot2_args")
checkmate::assert_subset(names(ggplot2_args), c("default", plot_choices))
args <- as.list(environment())
data_extract_list <- list(
arm_var = cs_to_des_select(arm_var, dataname = parentname),
paramcd = cs_to_des_filter(paramcd, dataname = dataname),
id_var = cs_to_des_select(id_var, dataname = dataname),
visit_var = cs_to_des_select(visit_var, dataname = dataname),
cov_var = cs_to_des_select(cov_var, dataname = dataname, multiple = TRUE),
split_covariates = cs_to_des_select(split_choices(cov_var), dataname = dataname, multiple = TRUE),
aval_var = cs_to_des_select(aval_var, dataname = dataname)
)
module(
label = label,
server = srv_mmrm,
ui = ui_mmrm,
ui_args = c(data_extract_list, args),
server_args = c(
data_extract_list,
list(
dataname = dataname,
parentname = parentname,
arm_ref_comp = arm_ref_comp,
label = label,
total_label = total_label,
plot_height = plot_height,
plot_width = plot_width,
basic_table_args = basic_table_args,
ggplot2_args = ggplot2_args
)
),
datanames = teal.transform::get_extract_datanames(data_extract_list)
)
}
#' @keywords internal
ui_mmrm <- function(id, ...) {
a <- list(...) # module args
ns <- NS(id)
is_single_dataset_value <- teal.transform::is_single_dataset(
a$arm_var,
a$paramcd,
a$id_var,
a$visit_var,
a$cov_var,
a$aval_var
)
tagList(
singleton(
tags$head(includeCSS(system.file("css/custom.css", package = "teal.modules.clinical")))
),
teal.widgets::standard_layout(
output = teal.widgets::white_small_well(
textOutput(ns("null_input_msg")),
tags$h3(textOutput(ns("mmrm_title"))),
teal.widgets::table_with_settings_ui(ns("mmrm_table")),
teal.widgets::plot_with_settings_ui(id = ns("mmrm_plot"))
),
encoding = tags$div(
### Reporter
teal.reporter::simple_reporter_ui(ns("simple_reporter")),
###
tags$label("Encodings", class = "text-primary"),
teal.transform::datanames_input(a[c("arm_var", "paramcd", "id_var", "visit_var", "cov_var", "aval_var")]),
teal.widgets::panel_group(
teal.widgets::panel_item(
"Model Settings",
teal.transform::data_extract_ui(
id = ns("aval_var"),
label = "Analysis Variable",
data_extract_spec = a$aval_var,
is_single_dataset = is_single_dataset_value
),
teal.transform::data_extract_ui(
id = ns("paramcd"),
label = "Select Endpoint",
data_extract_spec = a$paramcd,
is_single_dataset = is_single_dataset_value
),
teal.transform::data_extract_ui(
id = ns("visit_var"),
label = "Visit Variable",
data_extract_spec = a$visit_var,
is_single_dataset = is_single_dataset_value
),
teal.transform::data_extract_ui(
id = ns("cov_var"),
label = "Covariates",
data_extract_spec = a$cov_var,
is_single_dataset = is_single_dataset_value
),
shinyjs::hidden(
teal.transform::data_extract_ui(
id = ns("split_covariates"),
label = "Split Covariates",
data_extract_spec = a$split_covariates,
is_single_dataset = is_single_dataset_value
)
),
teal.transform::data_extract_ui(
id = ns("arm_var"),
label = "Select Treatment Variable",
data_extract_spec = a$arm_var,
is_single_dataset = is_single_dataset_value
),
shinyjs::hidden(uiOutput(ns("arms_buckets"))),
shinyjs::hidden(
helpText(
id = ns("help_text"), "Multiple reference groups are automatically combined into a single group."
)
),
shinyjs::hidden(
checkboxInput(
ns("combine_comp_arms"),
"Combine all comparison groups?",
value = FALSE
)
),
teal.transform::data_extract_ui(
id = ns("id_var"),
label = "Subject Identifier",
data_extract_spec = a$id_var,
is_single_dataset = is_single_dataset_value
),
selectInput(
ns("weights_emmeans"),
"Weights for LS means",
choices = c("proportional", "equal"),
selected = "proportional",
multiple = FALSE
),
selectInput(
ns("cor_struct"),
"Correlation Structure",
choices = eval(formals(tern.mmrm::build_formula)$cor_struct),
multiple = FALSE
),
teal.widgets::optionalSelectInput(
ns("method"),
"Adjustment Method",
a$method$choices,
a$method$selected,
multiple = FALSE,
fixed = a$method$fixed
),
teal.widgets::optionalSelectInput(
ns("conf_level"),
"Confidence Level",
a$conf_level$choices,
a$conf_level$selected,
multiple = FALSE,
fixed = a$conf_level$fixed
),
checkboxInput(
ns("parallel"),
"Parallel Computing",
value = TRUE
),
collapsed = FALSE # Start with having this panel opened.
)
),
actionButton(
ns("button_start"),
"Fit Model",
icon = icon("calculator"),
width = "100%",
class = "btn action-button text-dark bg-orange mb-4"
),
radioButtons(
ns("output_function"),
"Output Type",
choices = c(
"LS means table" = "t_mmrm_lsmeans",
"LS means plots" = "g_mmrm_lsmeans",
"Covariance estimate" = "t_mmrm_cov",
"Fixed effects" = "t_mmrm_fixed",
"Fit statistics" = "t_mmrm_diagnostic",
"Diagnostic plots" = "g_mmrm_diagnostic"
),
selected = "t_mmrm_lsmeans"
),
conditionalPanel(
condition = paste0(
"input['", ns("output_function"), "'] == 't_mmrm_lsmeans'", " || ",
"input['", ns("output_function"), "'] == 'g_mmrm_lsmeans'", " || ",
"input['", ns("output_function"), "'] == 'g_mmrm_diagnostic'"
),
teal.widgets::panel_group(
teal.widgets::panel_item(
"Output Settings",
# Additional option for LS means table.
selectInput(
ns("t_mmrm_lsmeans_show_relative"),
"Show Relative Change",
choices = c("reduction", "increase", "none"),
selected = "reduction",
multiple = FALSE
),
checkboxGroupInput(
ns("g_mmrm_lsmeans_select"),
"LS means plots",
choices = c(
"Estimates" = "estimates",
"Contrasts" = "contrasts"
),
selected = c("estimates", "contrasts"),
inline = TRUE
),
sliderInput(
ns("g_mmrm_lsmeans_width"),
"CI bar width",
min = 0.1,
max = 1,
value = 0.6
),
checkboxInput(
ns("g_mmrm_lsmeans_contrasts_show_pval"),
"Show contrasts p-values",
value = FALSE
),
# Additional options for diagnostic plots.
radioButtons(
ns("g_mmrm_diagnostic_type"),
"Diagnostic plot type",
choices = c(
"Fitted vs. Residuals" = "fit-residual",
"Normal Q-Q Plot of Residuals" = "q-q-residual"
),
selected = NULL
),
sliderInput(
ns("g_mmrm_diagnostic_z_threshold"),
"Label observations above this threshold",
min = 0.1,
max = 10,
value = 3
)
)
)
)
)
),
forms = tagList(
teal.widgets::verbatim_popup_ui(ns("rcode"), "Show R code")
),
pre_output = a$pre_output,
post_output = a$post_output
)
}
#' @keywords internal
srv_mmrm <- function(id,
data,
reporter,
filter_panel_api,
dataname,
parentname,
arm_var,
paramcd,
id_var,
visit_var,
cov_var,
split_covariates,
aval_var,
arm_ref_comp,
label,
total_label,
plot_height,
plot_width,
basic_table_args,
ggplot2_args) {
with_reporter <- !missing(reporter) && inherits(reporter, "Reporter")
with_filter <- !missing(filter_panel_api) && inherits(filter_panel_api, "FilterPanelAPI")
checkmate::assert_class(data, "reactive")
checkmate::assert_class(isolate(data()), "teal_data")
moduleServer(id, function(input, output, session) {
if (shiny::isRunning()) logger::log_shiny_input_changes(input, namespace = "teal.modules.clinical")
# Reactive responsible for sending a disable/enable signal
# to show R code and debug info buttons
disable_r_code <- reactiveVal(FALSE)
observeEvent(input[[extract_input("cov_var", dataname)]], {
# update covariates as actual variables
split_interactions_values <- split_interactions(input[[extract_input("cov_var", dataname)]])
arm_var_value <- input[[extract_input("arm_var", parentname)]]
arm_in_cov <- length(intersect(split_interactions_values, arm_var_value)) >= 1L
if (arm_in_cov) {
split_covariates_selected <- setdiff(split_interactions_values, arm_var_value)
} else {
split_covariates_selected <- split_interactions_values
}
teal.widgets::updateOptionalSelectInput(
session,
inputId = extract_input("split_covariates", dataname),
selected = split_covariates_selected
)
})
arm_ref_comp_iv <- arm_ref_comp_observer(
session,
input,
output,
id_arm_var = extract_input("arm_var", parentname), # From UI.
data = reactive(data()[[parentname]]),
arm_ref_comp = arm_ref_comp,
module = "tm_mmrm"
)
selector_list <- teal.transform::data_extract_multiple_srv(
data_extract = list(
arm_var = arm_var,
paramcd = paramcd,
id_var = id_var,
visit_var = visit_var,
split_covariates = split_covariates,
cov_var = cov_var, # only needed for validation see selector_list_without_cov reactive
aval_var = aval_var
),
datasets = data,
select_validation_rule = list(
aval_var = shinyvalidate::sv_required("'Analysis Variable' field is not selected"),
visit_var = shinyvalidate::sv_required("'Visit Variable' field is not selected"),
arm_var = shinyvalidate::sv_required("'Treatment Variable' field is not selected"),
id_var = shinyvalidate::sv_required("'Subject Identifier' field is not selected"),
# validation on cov_var
cov_var = function(value) {
if (length(selector_list()$visit_var()$select) == 0) {
return(NULL)
}
if ("BASE:AVISIT" %in% value && selector_list()$visit_var()$select == "AVISITN") {
paste(
"'BASE:AVISIT' is not a valid covariate when 'AVISITN' is selected as visit variable.",
"Please deselect 'BASE:AVISIT' as a covariate or change visit variable to 'AVISIT'."
)
} else if ("BASE:AVISITN" %in% value && selector_list()$visit_var()$select == "AVISIT") {
paste(
"'BASE:AVISITN' is not a valid covariate when 'AVISIT' is selected as visit variable.",
"Please deselect 'BASE:AVISITN' as a covariate or change visit variable to 'AVISITN'."
)
}
}
),
filter_validation_rule = list(
paramcd = shinyvalidate::sv_required("'Select Endpoint' field is not selected")
)
)
# selector_list includes cov_var as it is needed for validation rules
# but not needed to merge so it is removed here
selector_list_without_cov <- reactive({
selector_list()[names(selector_list()) != "cov_var"]
})
iv_r <- reactive({
iv <- shinyvalidate::InputValidator$new()
iv$add_validator(arm_ref_comp_iv)
iv$add_rule("conf_level", shinyvalidate::sv_required("'Confidence Level' field is not selected"))
iv$add_rule(
"conf_level",
shinyvalidate::sv_between(
0, 1,
message_fmt = "Confidence level must be between 0 and 1"
)
)
teal.transform::compose_and_enable_validators(iv, selector_list)
})
anl_inputs <- teal.transform::merge_expression_srv(
datasets = data,
selector_list = selector_list_without_cov,
merge_function = "dplyr::inner_join"
)
adsl_merge_inputs <- teal.transform::merge_expression_module(
datasets = data,
data_extract = list(arm_var = arm_var),
anl_name = "ANL_ADSL"
)
anl_q <- reactive({
data() %>%
teal.code::eval_code(code = as.expression(anl_inputs()$expr)) %>%
teal.code::eval_code(code = as.expression(adsl_merge_inputs()$expr))
})
# Initially hide the output title because there is no output yet.
shinyjs::hide("mmrm_title")
# reactiveVal used to send a signal to plot_with_settings module to hide the UI
show_plot_rv <- reactiveVal(FALSE)
# this will store the current/last state of inputs and data that generated a model-fit
# its purpose is to allow any input change to be checked whether it resulted in an out of sync state
state <- reactiveValues(input = NULL, button_start = 0)
# Note:
# input$parallel does not get us out of sync (it just takes longer to get to same result)
sync_inputs <- c(
extract_input("aval_var", dataname),
extract_input("paramcd", dataname, filter = TRUE),
extract_input("arm_var", parentname),
"Ref",
"Comp",
"combine_comp_arms",
extract_input("visit_var", dataname),
extract_input("cov_var", dataname),
extract_input("id_var", dataname),
"weights_emmeans",
"cor_struct",
"method",
"conf_level"
)
# Setup arm variable selection, default reference arms, and default
# comparison arms for encoding panel.
observeEvent(adsl_merge_inputs()$columns_source$arm_var, {
arm_var <- as.vector(adsl_merge_inputs()$columns_source$arm_var)
if (length(arm_var) == 0) {
shinyjs::hide("arms_buckets")
shinyjs::hide("help_text")
shinyjs::hide("combine_comp_arms")
} else {
shinyjs::show("arms_buckets")
shinyjs::show("help_text")
shinyjs::show("combine_comp_arms")
}
})